Overview

Dataset statistics

Number of variables89
Number of observations82255
Missing cells4769225
Missing cells (%)65.1%
Duplicate rows7
Duplicate rows (%)< 0.1%
Total size in memory55.9 MiB
Average record size in memory712.0 B

Variable types

Categorical47
Numeric22
Text7
Unsupported6
DateTime7

Alerts

oh_ClientCode has constant value ""Constant
oh_OrderVoucherValue has constant value ""Constant
oh_DeliveryTaxCode has constant value ""Constant
oh_DeliveryReasonCode has constant value ""Constant
oh_DiscountReasonCode has constant value ""Constant
oh_LoyaltyValue has constant value ""Constant
oh_OrderMethod has constant value ""Constant
oh_Priority has constant value ""Constant
oh_UserID has constant value ""Constant
oh_CurrencyCode has constant value ""Constant
oh_CurrencyRate has constant value ""Constant
oh_ShippingReasonCode has constant value ""Constant
oh_PII has constant value ""Constant
ol_ClientCode has constant value ""Constant
ol_PaymentMinorStatus has constant value ""Constant
ol_ParentItemNumber has constant value ""Constant
ol_Priority has constant value ""Constant
ol_UserID has constant value ""Constant
ol_CurrencyCode has constant value ""Constant
ol_CurrencyRate has constant value ""Constant
ol_DiscountReasonCode has constant value ""Constant
ol_TaxLocale has constant value ""Constant
ol_PII has constant value ""Constant
Dataset has 7 (< 0.1%) duplicate rowsDuplicates
RTYPE is highly overall correlated with SYS_CHANGE_OPERATION and 24 other fieldsHigh correlation
SYS_CHANGE_OPERATION is highly overall correlated with RTYPE and 19 other fieldsHigh correlation
SYS_CHANGE_VERSION is highly overall correlated with ol_ShippingCodeHigh correlation
oh_CampaignCode is highly overall correlated with RTYPE and 6 other fieldsHigh correlation
oh_CustomerID is highly overall correlated with ol_CustomerID and 1 other fieldsHigh correlation
oh_DeliveryGrossValue is highly overall correlated with RTYPE and 4 other fieldsHigh correlation
oh_DeliveryNetValue is highly overall correlated with RTYPE and 4 other fieldsHigh correlation
oh_DeliveryTaxValue is highly overall correlated with RTYPE and 4 other fieldsHigh correlation
oh_DiscountValue is highly overall correlated with ol_DiscountValueHigh correlation
oh_LastActionCode is highly overall correlated with RTYPE and 5 other fieldsHigh correlation
oh_NetOrderValue is highly overall correlated with oh_OrderGrossValue and 3 other fieldsHigh correlation
oh_OrderGrossValue is highly overall correlated with oh_NetOrderValue and 3 other fieldsHigh correlation
oh_OrderID is highly overall correlated with ol_InvoiceNumber and 1 other fieldsHigh correlation
oh_OrderMajorStatus is highly overall correlated with RTYPE and 6 other fieldsHigh correlation
oh_OrderMinorStatus is highly overall correlated with RTYPE and 3 other fieldsHigh correlation
oh_OrderType is highly overall correlated with RTYPE and 2 other fieldsHigh correlation
oh_OrderValuePaid is highly overall correlated with oh_NetOrderValue and 3 other fieldsHigh correlation
oh_OrderValueRefunded is highly overall correlated with SYS_CHANGE_OPERATION and 4 other fieldsHigh correlation
oh_PaymentMethod is highly overall correlated with RTYPE and 7 other fieldsHigh correlation
oh_ShippingCode is highly overall correlated with RTYPE and 9 other fieldsHigh correlation
oh_SourceCode is highly overall correlated with RTYPE and 6 other fieldsHigh correlation
oh_TaxValue is highly overall correlated with oh_NetOrderValue and 3 other fieldsHigh correlation
ol_AmountPaid is highly overall correlated with ol_AmountRefunded and 7 other fieldsHigh correlation
ol_AmountRefunded is highly overall correlated with ol_AmountPaid and 7 other fieldsHigh correlation
ol_CampaignCode is highly overall correlated with RTYPE and 6 other fieldsHigh correlation
ol_CustomerID is highly overall correlated with oh_CustomerID and 1 other fieldsHigh correlation
ol_DiscountValue is highly overall correlated with oh_DiscountValueHigh correlation
ol_GrossValue is highly overall correlated with ol_AmountPaid and 7 other fieldsHigh correlation
ol_InvoiceNumber is highly overall correlated with oh_OrderID and 2 other fieldsHigh correlation
ol_ItemUnitPrice is highly overall correlated with ol_AmountPaid and 7 other fieldsHigh correlation
ol_LastActionCode is highly overall correlated with RTYPE and 5 other fieldsHigh correlation
ol_LineID is highly overall correlated with ol_ShippingCodeHigh correlation
ol_LineMajorStatus is highly overall correlated with RTYPE and 9 other fieldsHigh correlation
ol_LineMinorStatus is highly overall correlated with RTYPE and 8 other fieldsHigh correlation
ol_NetAmount is highly overall correlated with ol_AmountPaid and 8 other fieldsHigh correlation
ol_OrderID is highly overall correlated with oh_OrderID and 1 other fieldsHigh correlation
ol_PaymentMajorStatus is highly overall correlated with RTYPE and 8 other fieldsHigh correlation
ol_PaymentMethod is highly overall correlated with RTYPE and 7 other fieldsHigh correlation
ol_Quantity is highly overall correlated with RTYPE and 3 other fieldsHigh correlation
ol_QuantityDespatched is highly overall correlated with ol_Quantity and 1 other fieldsHigh correlation
ol_ShippingCode is highly overall correlated with RTYPE and 17 other fieldsHigh correlation
ol_SkuStatus is highly overall correlated with RTYPE and 8 other fieldsHigh correlation
ol_SkuStockStatus is highly overall correlated with RTYPE and 7 other fieldsHigh correlation
ol_SourceCode is highly overall correlated with RTYPE and 6 other fieldsHigh correlation
ol_TaxRate is highly overall correlated with RTYPE and 2 other fieldsHigh correlation
ol_TaxValue is highly overall correlated with ol_AmountPaid and 8 other fieldsHigh correlation
ol_Weight is highly overall correlated with RTYPE and 7 other fieldsHigh correlation
RTYPE is highly imbalanced (> 99.9%)Imbalance
oh_CampaignCode is highly imbalanced (85.5%)Imbalance
oh_SourceCode is highly imbalanced (67.1%)Imbalance
oh_PaymentMethod is highly imbalanced (94.2%)Imbalance
oh_DeliveryNetValue is highly imbalanced (75.5%)Imbalance
oh_DeliveryTaxValue is highly imbalanced (72.0%)Imbalance
oh_DeliveryGrossValue is highly imbalanced (71.9%)Imbalance
oh_OrderType is highly imbalanced (92.3%)Imbalance
oh_OrderMajorStatus is highly imbalanced (67.8%)Imbalance
oh_OrderMinorStatus is highly imbalanced (97.3%)Imbalance
oh_LastActionCode is highly imbalanced (79.9%)Imbalance
oh_ShippingCode is highly imbalanced (51.7%)Imbalance
ol_CampaignCode is highly imbalanced (85.5%)Imbalance
ol_SourceCode is highly imbalanced (67.1%)Imbalance
ol_Quantity is highly imbalanced (97.3%)Imbalance
ol_LineMajorStatus is highly imbalanced (65.3%)Imbalance
ol_LineMinorStatus is highly imbalanced (53.4%)Imbalance
ol_PaymentMethod is highly imbalanced (94.2%)Imbalance
ol_TaxRate is highly imbalanced (68.8%)Imbalance
SYS_CHANGE_VERSION has 74064 (90.0%) missing valuesMissing
oh_OrderID has 74064 (90.0%) missing valuesMissing
oh_CustomerID has 74064 (90.0%) missing valuesMissing
oh_MediaID has 82252 (> 99.9%) missing valuesMissing
oh_PaymentMethod has 74064 (90.0%) missing valuesMissing
oh_PaymentType has 82255 (100.0%) missing valuesMissing
oh_NetOrderValue has 74064 (90.0%) missing valuesMissing
oh_TaxValue has 74064 (90.0%) missing valuesMissing
oh_OrderGrossValue has 74064 (90.0%) missing valuesMissing
oh_OrderValuePaid has 74064 (90.0%) missing valuesMissing
oh_OrderValueRefunded has 49491 (60.2%) missing valuesMissing
oh_OrderVoucherValue has 49491 (60.2%) missing valuesMissing
oh_DeliveryTaxCode has 49491 (60.2%) missing valuesMissing
oh_DeliveryNetValue has 74064 (90.0%) missing valuesMissing
oh_DeliveryTaxValue has 74064 (90.0%) missing valuesMissing
oh_DeliveryGrossValue has 74064 (90.0%) missing valuesMissing
oh_DeliveryReasonCode has 82254 (> 99.9%) missing valuesMissing
oh_DiscountValue has 74064 (90.0%) missing valuesMissing
oh_DiscountReasonCode has 71543 (87.0%) missing valuesMissing
oh_LoyaltyValue has 74064 (90.0%) missing valuesMissing
oh_OrderMethod has 74064 (90.0%) missing valuesMissing
oh_OrderType has 74064 (90.0%) missing valuesMissing
oh_DespatchDate has 82255 (100.0%) missing valuesMissing
oh_CancelledDate has 82216 (> 99.9%) missing valuesMissing
oh_Priority has 74064 (90.0%) missing valuesMissing
oh_UserID has 74064 (90.0%) missing valuesMissing
oh_CurrencyCode has 3734 (4.5%) missing valuesMissing
oh_CurrencyRate has 74064 (90.0%) missing valuesMissing
oh_LastActionCode has 79234 (96.3%) missing valuesMissing
oh_DueDate has 3969 (4.8%) missing valuesMissing
oh_DeliverByDate has 3969 (4.8%) missing valuesMissing
oh_ShippingReasonCode has 82254 (> 99.9%) missing valuesMissing
ol_OrderID has 74064 (90.0%) missing valuesMissing
ol_CustomerID has 74064 (90.0%) missing valuesMissing
ol_LineID has 74064 (90.0%) missing valuesMissing
ol_MediaID has 82252 (> 99.9%) missing valuesMissing
ol_Quantity has 74064 (90.0%) missing valuesMissing
ol_QuantityDespatched has 74064 (90.0%) missing valuesMissing
ol_DespatchedDate has 15965 (19.4%) missing valuesMissing
ol_LineMinorStatus has 26934 (32.7%) missing valuesMissing
ol_SkuStatus has 74064 (90.0%) missing valuesMissing
ol_SkuStockStatus has 74064 (90.0%) missing valuesMissing
ol_PaymentMajorStatus has 30576 (37.2%) missing valuesMissing
ol_PaymentMinorStatus has 30577 (37.2%) missing valuesMissing
ol_PaymentMethod has 74064 (90.0%) missing valuesMissing
ol_PaymentType has 82255 (100.0%) missing valuesMissing
ol_CancelledDate has 78136 (95.0%) missing valuesMissing
ol_LastActionCode has 79709 (96.9%) missing valuesMissing
ol_ParentItemNumber has 74064 (90.0%) missing valuesMissing
ol_Priority has 74064 (90.0%) missing valuesMissing
ol_UserID has 74064 (90.0%) missing valuesMissing
ol_CurrencyCode has 3734 (4.5%) missing valuesMissing
ol_CurrencyRate has 74064 (90.0%) missing valuesMissing
ol_ShippingCode has 81996 (99.7%) missing valuesMissing
ol_InvoiceNumber has 74064 (90.0%) missing valuesMissing
ol_ItemCostPrice has 74064 (90.0%) missing valuesMissing
ol_ItemUnitPrice has 74064 (90.0%) missing valuesMissing
ol_NetAmount has 74064 (90.0%) missing valuesMissing
ol_TaxValue has 74064 (90.0%) missing valuesMissing
ol_GrossValue has 74064 (90.0%) missing valuesMissing
ol_AmountPaid has 74064 (90.0%) missing valuesMissing
ol_AmountRefunded has 78577 (95.5%) missing valuesMissing
ol_PostageValue has 82255 (100.0%) missing valuesMissing
ol_PostageTaxValue has 82255 (100.0%) missing valuesMissing
ol_PostageReasonCode has 82255 (100.0%) missing valuesMissing
ol_DiscountValue has 74064 (90.0%) missing valuesMissing
ol_DiscountReasonCode has 81831 (99.5%) missing valuesMissing
ol_TaxLocale has 82252 (> 99.9%) missing valuesMissing
ol_TaxRate has 74064 (90.0%) missing valuesMissing
ol_Weight has 74064 (90.0%) missing valuesMissing
ol_ItemCostPrice is highly skewed (γ1 = 23.07598238)Skewed
oh_PaymentType is an unsupported type, check if it needs cleaning or further analysisUnsupported
oh_DespatchDate is an unsupported type, check if it needs cleaning or further analysisUnsupported
ol_PaymentType is an unsupported type, check if it needs cleaning or further analysisUnsupported
ol_PostageValue is an unsupported type, check if it needs cleaning or further analysisUnsupported
ol_PostageTaxValue is an unsupported type, check if it needs cleaning or further analysisUnsupported
ol_PostageReasonCode is an unsupported type, check if it needs cleaning or further analysisUnsupported
oh_OrderValueRefunded has 24893 (30.3%) zerosZeros
oh_DiscountValue has 7411 (9.0%) zerosZeros
ol_ItemCostPrice has 8145 (9.9%) zerosZeros
ol_ItemUnitPrice has 2774 (3.4%) zerosZeros
ol_NetAmount has 2579 (3.1%) zerosZeros
ol_TaxValue has 3037 (3.7%) zerosZeros
ol_GrossValue has 2579 (3.1%) zerosZeros
ol_AmountPaid has 2631 (3.2%) zerosZeros
ol_DiscountValue has 7796 (9.5%) zerosZeros

Reproduction

Analysis started2023-11-24 14:22:35.340067
Analysis finished2023-11-24 14:24:18.131407
Duration1 minute and 42.79 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

RTYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size642.7 KiB
H
82254 
(82254 rows affected)
 
1

Length

Max length21
Median length1
Mean length1.0002431
Min length1

Characters and Unicode

Total characters82275
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowH
2nd rowH
3rd rowH
4th rowH
5th rowH

Common Values

ValueCountFrequency (%)
H 82254
> 99.9%
(82254 rows affected) 1
 
< 0.1%

Length

2023-11-24T14:24:18.211720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:18.328817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
h 82254
> 99.9%
82254 1
 
< 0.1%
rows 1
 
< 0.1%
affected 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
H 82254
> 99.9%
2 2
 
< 0.1%
2
 
< 0.1%
e 2
 
< 0.1%
f 2
 
< 0.1%
s 1
 
< 0.1%
d 1
 
< 0.1%
t 1
 
< 0.1%
c 1
 
< 0.1%
a 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 82254
> 99.9%
Lowercase Letter 12
 
< 0.1%
Decimal Number 5
 
< 0.1%
Space Separator 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2
16.7%
f 2
16.7%
s 1
8.3%
d 1
8.3%
t 1
8.3%
c 1
8.3%
a 1
8.3%
w 1
8.3%
o 1
8.3%
r 1
8.3%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
4 1
20.0%
5 1
20.0%
8 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
H 82254
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82266
> 99.9%
Common 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 82254
> 99.9%
e 2
 
< 0.1%
f 2
 
< 0.1%
s 1
 
< 0.1%
d 1
 
< 0.1%
t 1
 
< 0.1%
c 1
 
< 0.1%
a 1
 
< 0.1%
w 1
 
< 0.1%
o 1
 
< 0.1%
Common
ValueCountFrequency (%)
2 2
22.2%
2
22.2%
( 1
11.1%
4 1
11.1%
5 1
11.1%
8 1
11.1%
) 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 82254
> 99.9%
2 2
 
< 0.1%
2
 
< 0.1%
e 2
 
< 0.1%
f 2
 
< 0.1%
s 1
 
< 0.1%
d 1
 
< 0.1%
t 1
 
< 0.1%
c 1
 
< 0.1%
a 1
 
< 0.1%
Other values (8) 8
 
< 0.1%

SYS_CHANGE_VERSION
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)1.0%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean14184.347
Minimum77
Maximum26519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:18.438355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile947.5
Q18897
median12122
Q322188
95-th percentile25267
Maximum26519
Range26442
Interquartile range (IQR)13291

Descriptive statistics

Standard deviation7666.6463
Coefficient of variation (CV)0.54050048
Kurtosis-1.1874222
Mean14184.347
Median Absolute Deviation (MAD)5952
Skewness0.065546675
Sum1.1618398 × 108
Variance58777465
MonotonicityNot monotonic
2023-11-24T14:24:18.586993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8897 620
 
0.8%
11378 542
 
0.7%
10602 536
 
0.7%
7674 507
 
0.6%
22995 454
 
0.6%
9615 425
 
0.5%
25267 417
 
0.5%
12122 401
 
0.5%
77 400
 
0.5%
21227 385
 
0.5%
Other values (71) 3504
 
4.3%
(Missing) 74064
90.0%
ValueCountFrequency (%)
77 400
0.5%
211 4
 
< 0.1%
853 6
 
< 0.1%
1042 136
 
0.2%
1425 30
 
< 0.1%
1835 10
 
< 0.1%
2440 9
 
< 0.1%
3009 3
 
< 0.1%
4053 2
 
< 0.1%
5928 157
 
0.2%
ValueCountFrequency (%)
26519 3
 
< 0.1%
26480 4
 
< 0.1%
26469 3
 
< 0.1%
26426 4
 
< 0.1%
26423 2
 
< 0.1%
26401 95
0.1%
26399 17
 
< 0.1%
25882 6
 
< 0.1%
25864 209
0.3%
25746 6
 
< 0.1%

SYS_CHANGE_OPERATION
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
U
41408 
I
40846 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters82254
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 41408
50.3%
I 40846
49.7%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:18.717308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:18.831590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
u 41408
50.3%
i 40846
49.7%

Most occurring characters

ValueCountFrequency (%)
U 41408
50.3%
I 40846
49.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 82254
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 41408
50.3%
I 40846
49.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 82254
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 41408
50.3%
I 40846
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 41408
50.3%
I 40846
49.7%

oh_ClientCode
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
CRW
82254 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters246762
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCRW
2nd rowCRW
3rd rowCRW
4th rowCRW
5th rowCRW

Common Values

ValueCountFrequency (%)
CRW 82254
> 99.9%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:18.928123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:19.034024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
crw 82254
100.0%

Most occurring characters

ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 246762
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 246762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

oh_OrderID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2113
Distinct (%)25.8%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean6519444.4
Minimum5421459
Maximum6555985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:19.127807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5421459
5-th percentile6471496
Q16512281.5
median6525779
Q36537162
95-th percentile6550271.5
Maximum6555985
Range1134526
Interquartile range (IQR)24880.5

Descriptive statistics

Standard deviation41613.862
Coefficient of variation (CV)0.0063830381
Kurtosis191.72675
Mean6519444.4
Median Absolute Deviation (MAD)12142
Skewness-10.705328
Sum5.3400769 × 1010
Variance1.7317135 × 109
MonotonicityNot monotonic
2023-11-24T14:24:19.258459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6514411 25
 
< 0.1%
6489908 19
 
< 0.1%
6552899 18
 
< 0.1%
6541399 17
 
< 0.1%
6474533 16
 
< 0.1%
6533665 15
 
< 0.1%
6522805 15
 
< 0.1%
6533159 14
 
< 0.1%
6482742 14
 
< 0.1%
6542958 14
 
< 0.1%
Other values (2103) 8024
 
9.8%
(Missing) 74064
90.0%
ValueCountFrequency (%)
5421459 2
 
< 0.1%
5864333 2
 
< 0.1%
5942595 4
< 0.1%
5983878 7
< 0.1%
6048975 4
< 0.1%
6081160 4
< 0.1%
6111623 4
< 0.1%
6334792 2
 
< 0.1%
6344916 4
< 0.1%
6345788 3
< 0.1%
ValueCountFrequency (%)
6555985 1
 
< 0.1%
6555965 9
< 0.1%
6555919 2
 
< 0.1%
6555892 9
< 0.1%
6555809 2
 
< 0.1%
6555777 3
 
< 0.1%
6555770 3
 
< 0.1%
6555726 2
 
< 0.1%
6555724 2
 
< 0.1%
6555650 6
< 0.1%
Distinct23772
Distinct (%)29.0%
Missing339
Missing (%)0.4%
Memory size642.7 KiB
2023-11-24T14:24:19.721895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length16
Median length8
Mean length8.1055471
Min length5

Characters and Unicode

Total characters663974
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)3.0%

Sample

1st row12841488
2nd row12841488
3rd row13158853
4th row13158853
5th row13217192
ValueCountFrequency (%)
13715808 32
 
< 0.1%
13660449 25
 
< 0.1%
13719960 24
 
< 0.1%
13710909 23
 
< 0.1%
13707110 23
 
< 0.1%
13666197 21
 
< 0.1%
13714759 20
 
< 0.1%
13719757 20
 
< 0.1%
13708042 20
 
< 0.1%
13704457 20
 
< 0.1%
Other values (23762) 81688
99.7%
2023-11-24T14:24:20.303649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 147525
22.2%
3 119543
18.0%
7 103507
15.6%
0 69125
10.4%
6 44337
 
6.7%
2 36135
 
5.4%
8 35524
 
5.4%
9 35514
 
5.3%
4 33863
 
5.1%
5 33859
 
5.1%
Other values (9) 5042
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658932
99.2%
Uppercase Letter 4716
 
0.7%
Dash Punctuation 326
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147525
22.4%
3 119543
18.1%
7 103507
15.7%
0 69125
10.5%
6 44337
 
6.7%
2 36135
 
5.5%
8 35524
 
5.4%
9 35514
 
5.4%
4 33863
 
5.1%
5 33859
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
Y 3889
82.5%
V 163
 
3.5%
E 163
 
3.5%
R 163
 
3.5%
O 163
 
3.5%
S 114
 
2.4%
M 49
 
1.0%
X 12
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 659258
99.3%
Latin 4716
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 147525
22.4%
3 119543
18.1%
7 103507
15.7%
0 69125
10.5%
6 44337
 
6.7%
2 36135
 
5.5%
8 35524
 
5.4%
9 35514
 
5.4%
4 33863
 
5.1%
5 33859
 
5.1%
Latin
ValueCountFrequency (%)
Y 3889
82.5%
V 163
 
3.5%
E 163
 
3.5%
R 163
 
3.5%
O 163
 
3.5%
S 114
 
2.4%
M 49
 
1.0%
X 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147525
22.2%
3 119543
18.0%
7 103507
15.6%
0 69125
10.4%
6 44337
 
6.7%
2 36135
 
5.4%
8 35524
 
5.4%
9 35514
 
5.3%
4 33863
 
5.1%
5 33859
 
5.1%
Other values (9) 5042
 
0.8%

oh_CustomerID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2037
Distinct (%)24.9%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean24851252
Minimum10512
Maximum34101675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:20.461896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10512
5-th percentile9959870
Q117903003
median25847047
Q333849878
95-th percentile34033050
Maximum34101675
Range34091163
Interquartile range (IQR)15946875

Descriptive statistics

Standard deviation8493903.9
Coefficient of variation (CV)0.34178978
Kurtosis-0.9286762
Mean24851252
Median Absolute Deviation (MAD)7998564
Skewness-0.46525263
Sum2.035566 × 1011
Variance7.2146403 × 1013
MonotonicityNot monotonic
2023-11-24T14:24:20.603008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16635245 26
 
< 0.1%
10425049 25
 
< 0.1%
15614761 24
 
< 0.1%
33836149 19
 
< 0.1%
34087130 18
 
< 0.1%
17181934 17
 
< 0.1%
33912221 17
 
< 0.1%
17162728 16
 
< 0.1%
33779356 16
 
< 0.1%
34009472 15
 
< 0.1%
Other values (2027) 7998
 
9.7%
(Missing) 74064
90.0%
ValueCountFrequency (%)
10512 7
< 0.1%
494757 3
< 0.1%
691469 3
< 0.1%
989806 3
< 0.1%
1848530 3
< 0.1%
3014983 4
< 0.1%
3396223 3
< 0.1%
3767001 3
< 0.1%
3799000 6
< 0.1%
3809340 7
< 0.1%
ValueCountFrequency (%)
34101675 1
 
< 0.1%
34101451 2
 
< 0.1%
34100990 2
 
< 0.1%
34100123 6
< 0.1%
34100040 4
< 0.1%
34099788 2
 
< 0.1%
34099705 2
 
< 0.1%
34097493 3
< 0.1%
34096024 3
< 0.1%
34095323 4
< 0.1%

oh_CampaignCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
WEBT
76301 
JLP
 
3726
WEBC
 
1084
UKMO
 
355
GLBE
 
308
Other values (6)
 
480

Length

Max length4
Median length4
Mean length3.9545919
Min length2

Characters and Unicode

Total characters325281
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWEBT
2nd rowWEBT
3rd rowWEBT
4th rowWEBT
5th rowWEBT

Common Values

ValueCountFrequency (%)
WEBT 76301
92.8%
JLP 3726
 
4.5%
WEBC 1084
 
1.3%
UKMO 355
 
0.4%
GLBE 308
 
0.4%
GIFT 234
 
0.3%
VERY 163
 
0.2%
NXPP 76
 
0.1%
CAT 4
 
< 0.1%
MO 2
 
< 0.1%

Length

2023-11-24T14:24:20.778520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
webt 76301
92.8%
jlp 3726
 
4.5%
webc 1084
 
1.3%
ukmo 355
 
0.4%
glbe 308
 
0.4%
gift 234
 
0.3%
very 163
 
0.2%
nxpp 76
 
0.1%
cat 4
 
< 0.1%
mo 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 325281
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 325281
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

oh_SourceCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
WEB
64871 
CLIC
7538 
AW
 
5851
JLP
 
3726
VERY
 
163
Other values (5)
 
105

Length

Max length10
Median length3
Mean length3.0245702
Min length2

Characters and Unicode

Total characters248783
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWEB
2nd rowWEB
3rd rowWEB
4th rowWEB
5th rowWEB

Common Values

ValueCountFrequency (%)
WEB 64871
78.9%
CLIC 7538
 
9.2%
AW 5851
 
7.1%
JLP 3726
 
4.5%
VERY 163
 
0.2%
NXPP 76
 
0.1%
AWSRC=AW 13
 
< 0.1%
AT01 9
 
< 0.1%
CAT 4
 
< 0.1%
MAIL ORDER 3
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:20.912807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:21.052666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
web 64871
78.9%
clic 7538
 
9.2%
aw 5851
 
7.1%
jlp 3726
 
4.5%
very 163
 
0.2%
nxpp 76
 
0.1%
awsrc=aw 13
 
< 0.1%
at01 9
 
< 0.1%
cat 4
 
< 0.1%
mail 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (13) 547
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 248749
> 99.9%
Decimal Number 18
 
< 0.1%
Math Symbol 13
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (9) 513
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 9
50.0%
1 9
50.0%
Math Symbol
ValueCountFrequency (%)
= 13
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 248749
> 99.9%
Common 34
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (9) 513
 
0.2%
Common
ValueCountFrequency (%)
= 13
38.2%
0 9
26.5%
1 9
26.5%
3
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (13) 547
 
0.2%

oh_MediaID
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing82252
Missing (%)> 99.9%
Memory size642.7 KiB
2023-11-24T14:24:21.181552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length20
Median length11
Mean length14
Min length11

Characters and Unicode

Total characters42
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowPHONE Order
2nd rowPHONE Order
3rd rowVerifone PHONE Order
ValueCountFrequency (%)
phone 3
42.9%
order 3
42.9%
verifone 1
 
14.3%
2023-11-24T14:24:21.393647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7
16.7%
O 6
14.3%
e 5
11.9%
4
9.5%
P 3
7.1%
H 3
7.1%
N 3
7.1%
E 3
7.1%
d 3
7.1%
V 1
 
2.4%
Other values (4) 4
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19
45.2%
Uppercase Letter 19
45.2%
Space Separator 4
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7
36.8%
e 5
26.3%
d 3
15.8%
i 1
 
5.3%
f 1
 
5.3%
o 1
 
5.3%
n 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 6
31.6%
P 3
15.8%
H 3
15.8%
N 3
15.8%
E 3
15.8%
V 1
 
5.3%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38
90.5%
Common 4
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7
18.4%
O 6
15.8%
e 5
13.2%
P 3
7.9%
H 3
7.9%
N 3
7.9%
E 3
7.9%
d 3
7.9%
V 1
 
2.6%
i 1
 
2.6%
Other values (3) 3
7.9%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7
16.7%
O 6
14.3%
e 5
11.9%
4
9.5%
P 3
7.1%
H 3
7.1%
N 3
7.1%
E 3
7.1%
d 3
7.1%
V 1
 
2.4%
Other values (4) 4
9.5%

oh_PaymentMethod
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
2.0
8107 
4.0
 
45
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 8107
 
9.9%
4.0 45
 
0.1%
1.0 39
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:21.520400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:21.634804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 8107
99.0%
4.0 45
 
0.5%
1.0 39
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8191
50.0%
2 8107
49.5%
4 45
 
0.3%
1 39
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

oh_PaymentType
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

oh_NetOrderValue
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1046
Distinct (%)12.8%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean126.17569
Minimum0
Maximum1081.39
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:21.753272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.55
Q158.76
median93.76
Q3153.13
95-th percentile314.42
Maximum1081.39
Range1081.39
Interquartile range (IQR)94.37

Descriptive statistics

Standard deviation109.16417
Coefficient of variation (CV)0.86517591
Kurtosis19.982003
Mean126.17569
Median Absolute Deviation (MAD)42.96
Skewness3.3985993
Sum1033505.1
Variance11916.815
MonotonicityNot monotonic
2023-11-24T14:24:21.899361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.76 158
 
0.2%
81.26 115
 
0.1%
43.13 87
 
0.1%
46.88 86
 
0.1%
40.17 75
 
0.1%
43.92 66
 
0.1%
43.75 60
 
0.1%
36.88 58
 
0.1%
77.51 56
 
0.1%
75 48
 
0.1%
Other values (1036) 7382
 
9.0%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2
< 0.1%
9.17 2
< 0.1%
10 2
< 0.1%
11.25 4
< 0.1%
11.67 2
< 0.1%
13 2
< 0.1%
13.29 4
< 0.1%
13.33 2
< 0.1%
13.75 2
< 0.1%
14.54 3
< 0.1%
ValueCountFrequency (%)
1081.39 25
< 0.1%
641.03 19
< 0.1%
628.94 17
< 0.1%
584.97 11
< 0.1%
568.16 8
 
< 0.1%
552.52 12
< 0.1%
500.06 15
< 0.1%
461.66 10
 
< 0.1%
455.04 11
< 0.1%
451.5 15
< 0.1%

oh_TaxValue
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct889
Distinct (%)10.9%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean23.466875
Minimum0
Maximum216.34
Zeros318
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:22.041359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.71
Q110
median17.38
Q329.48
95-th percentile61.5
Maximum216.34
Range216.34
Interquartile range (IQR)19.48

Descriptive statistics

Standard deviation21.583085
Coefficient of variation (CV)0.91972559
Kurtosis20.992706
Mean23.466875
Median Absolute Deviation (MAD)8.6
Skewness3.3963228
Sum192217.17
Variance465.82956
MonotonicityNot monotonic
2023-11-24T14:24:22.163614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 318
 
0.4%
14.74 163
 
0.2%
16.24 115
 
0.1%
9.37 104
 
0.1%
8.62 87
 
0.1%
7.37 78
 
0.1%
8.03 75
 
0.1%
8.78 66
 
0.1%
8.75 64
 
0.1%
8.12 62
 
0.1%
Other values (879) 7059
 
8.6%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 318
0.4%
0.66 46
 
0.1%
0.99 18
 
< 0.1%
1.5 5
 
< 0.1%
1.83 2
 
< 0.1%
2 2
 
< 0.1%
2.25 9
 
< 0.1%
2.33 2
 
< 0.1%
2.6 2
 
< 0.1%
2.66 4
 
< 0.1%
ValueCountFrequency (%)
216.34 25
< 0.1%
125.71 17
< 0.1%
116.98 11
< 0.1%
113.59 8
 
< 0.1%
110.48 12
< 0.1%
99.94 15
< 0.1%
93.47 19
< 0.1%
92.34 10
 
< 0.1%
90.96 11
< 0.1%
90.03 14
< 0.1%

oh_OrderGrossValue
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct870
Distinct (%)10.6%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean149.64256
Minimum0
Maximum1297.73
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:22.291752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.3
Q168.25
median111.5
Q3180.6
95-th percentile377.25
Maximum1297.73
Range1297.73
Interquartile range (IQR)112.35

Descriptive statistics

Standard deviation129.6657
Coefficient of variation (CV)0.86650279
Kurtosis20.718261
Mean149.64256
Median Absolute Deviation (MAD)51.5
Skewness3.4442517
Sum1225722.2
Variance16813.193
MonotonicityNot monotonic
2023-11-24T14:24:22.425172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.5 161
 
0.2%
97.5 119
 
0.1%
51.75 90
 
0.1%
56.25 89
 
0.1%
48.2 75
 
0.1%
52.7 66
 
0.1%
52.5 64
 
0.1%
93 60
 
0.1%
44.25 60
 
0.1%
90 57
 
0.1%
Other values (860) 7350
 
8.9%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2
< 0.1%
11 2
< 0.1%
12 2
< 0.1%
13.5 4
< 0.1%
14 2
< 0.1%
15.4 2
< 0.1%
15.6 2
< 0.1%
15.95 4
< 0.1%
16 2
< 0.1%
16.5 2
< 0.1%
ValueCountFrequency (%)
1297.73 25
< 0.1%
754.65 17
< 0.1%
734.5 19
< 0.1%
701.95 11
< 0.1%
681.75 8
 
< 0.1%
663 12
< 0.1%
600 15
< 0.1%
554 10
 
< 0.1%
546 11
< 0.1%
540.55 14
< 0.1%

oh_OrderValuePaid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct862
Distinct (%)10.5%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean148.97502
Minimum0
Maximum1297.73
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:22.575521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.3
Q167.6
median111
Q3180.25
95-th percentile376.5
Maximum1297.73
Range1297.73
Interquartile range (IQR)112.65

Descriptive statistics

Standard deviation128.57651
Coefficient of variation (CV)0.86307426
Kurtosis21.060107
Mean148.97502
Median Absolute Deviation (MAD)51.75
Skewness3.4459595
Sum1220254.4
Variance16531.918
MonotonicityNot monotonic
2023-11-24T14:24:22.729977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.5 161
 
0.2%
97.5 119
 
0.1%
51.75 90
 
0.1%
56.25 89
 
0.1%
48.2 75
 
0.1%
52.5 67
 
0.1%
52.7 66
 
0.1%
90 63
 
0.1%
44.25 63
 
0.1%
93 60
 
0.1%
Other values (852) 7338
 
8.9%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2
< 0.1%
11 2
< 0.1%
12 2
< 0.1%
13.5 4
< 0.1%
14 2
< 0.1%
15.4 2
< 0.1%
15.6 2
< 0.1%
15.95 4
< 0.1%
16 2
< 0.1%
16.5 2
< 0.1%
ValueCountFrequency (%)
1297.73 25
< 0.1%
734.5 19
< 0.1%
687.15 17
< 0.1%
681.75 8
 
< 0.1%
663 12
< 0.1%
657.7 11
< 0.1%
600 15
< 0.1%
554 10
 
< 0.1%
546 11
< 0.1%
540.55 14
< 0.1%

oh_OrderValueRefunded
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct575
Distinct (%)1.8%
Missing49491
Missing (%)60.2%
Infinite0
Infinite (%)0.0%
Mean23.85897
Minimum0
Maximum1097.59
Zeros24893
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:22.894008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile133.5
Maximum1097.59
Range1097.59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64.729386
Coefficient of variation (CV)2.7130001
Kurtosis70.815014
Mean23.85897
Median Absolute Deviation (MAD)0
Skewness6.3172168
Sum781715.28
Variance4189.8934
MonotonicityNot monotonic
2023-11-24T14:24:23.029524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24893
30.3%
44.25 416
 
0.5%
48.75 321
 
0.4%
56.25 161
 
0.2%
51.75 157
 
0.2%
45.5 136
 
0.2%
41.3 130
 
0.2%
88.5 122
 
0.1%
48.3 118
 
0.1%
97.5 97
 
0.1%
Other values (565) 6213
 
7.6%
(Missing) 49491
60.2%
ValueCountFrequency (%)
0 24893
30.3%
3.95 3
 
< 0.1%
5.95 3
 
< 0.1%
8 7
 
< 0.1%
11 5
 
< 0.1%
11.2 6
 
< 0.1%
11.25 3
 
< 0.1%
12 13
 
< 0.1%
13 3
 
< 0.1%
13.25 2
 
< 0.1%
ValueCountFrequency (%)
1097.59 25
< 0.1%
623.76 12
< 0.1%
586.5 19
< 0.1%
507 5
 
< 0.1%
480.75 13
< 0.1%
461.3 14
< 0.1%
457.5 11
< 0.1%
411 9
 
< 0.1%
388.31 7
 
< 0.1%
385.68 16
< 0.1%

oh_OrderVoucherValue
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing49491
Missing (%)60.2%
Memory size642.7 KiB
0.0
32764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98292
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 32764
39.8%
(Missing) 49491
60.2%

Length

2023-11-24T14:24:23.379756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:23.494255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 32764
100.0%

Most occurring characters

ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65528
66.7%
Other Punctuation 32764
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65528
100.0%
Other Punctuation
ValueCountFrequency (%)
. 32764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

oh_DeliveryTaxCode
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing49491
Missing (%)60.2%
Memory size642.7 KiB
0.0
32764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98292
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 32764
39.8%
(Missing) 49491
60.2%

Length

2023-11-24T14:24:23.586209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:23.714183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 32764
100.0%

Most occurring characters

ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65528
66.7%
Other Punctuation 32764
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65528
100.0%
Other Punctuation
ValueCountFrequency (%)
. 32764
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65528
66.7%
. 32764
33.3%

oh_DeliveryNetValue
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
7409 
3.29
 
461
4.96
 
290
2.92
 
25
3.95
 
6

Length

Max length4
Median length3
Mean length3.0954706
Min length3

Characters and Unicode

Total characters25355
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7409
 
9.0%
3.29 461
 
0.6%
4.96 290
 
0.4%
2.92 25
 
< 0.1%
3.95 6
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:23.823956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:23.948848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7409
90.5%
3.29 461
 
5.6%
4.96 290
 
3.5%
2.92 25
 
0.3%
3.95 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14818
58.4%
. 8191
32.3%
9 782
 
3.1%
2 511
 
2.0%
3 467
 
1.8%
4 290
 
1.1%
6 290
 
1.1%
5 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17164
67.7%
Other Punctuation 8191
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14818
86.3%
9 782
 
4.6%
2 511
 
3.0%
3 467
 
2.7%
4 290
 
1.7%
6 290
 
1.7%
5 6
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25355
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14818
58.4%
. 8191
32.3%
9 782
 
3.1%
2 511
 
2.0%
3 467
 
1.8%
4 290
 
1.1%
6 290
 
1.1%
5 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14818
58.4%
. 8191
32.3%
9 782
 
3.1%
2 511
 
2.0%
3 467
 
1.8%
4 290
 
1.1%
6 290
 
1.1%
5 6
 
< 0.1%

oh_DeliveryTaxValue
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
7415 
0.66
 
461
0.99
 
290
0.58
 
25

Length

Max length4
Median length3
Mean length3.0947381
Min length3

Characters and Unicode

Total characters25349
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7415
 
9.0%
0.66 461
 
0.6%
0.99 290
 
0.4%
0.58 25
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:24.050252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:24.153800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7415
90.5%
0.66 461
 
5.6%
0.99 290
 
3.5%
0.58 25
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15606
61.6%
. 8191
32.3%
6 922
 
3.6%
9 580
 
2.3%
5 25
 
0.1%
8 25
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17158
67.7%
Other Punctuation 8191
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15606
91.0%
6 922
 
5.4%
9 580
 
3.4%
5 25
 
0.1%
8 25
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25349
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15606
61.6%
. 8191
32.3%
6 922
 
3.6%
9 580
 
2.3%
5 25
 
0.1%
8 25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15606
61.6%
. 8191
32.3%
6 922
 
3.6%
9 580
 
2.3%
5 25
 
0.1%
8 25
 
0.1%

oh_DeliveryGrossValue
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
7409 
3.95
 
467
5.95
 
290
3.5
 
25

Length

Max length4
Median length3
Mean length3.0924185
Min length3

Characters and Unicode

Total characters25330
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7409
 
9.0%
3.95 467
 
0.6%
5.95 290
 
0.4%
3.5 25
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:24.245790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:24.350915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7409
90.5%
3.95 467
 
5.7%
5.95 290
 
3.5%
3.5 25
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 14818
58.5%
. 8191
32.3%
5 1072
 
4.2%
9 757
 
3.0%
3 492
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17139
67.7%
Other Punctuation 8191
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14818
86.5%
5 1072
 
6.3%
9 757
 
4.4%
3 492
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14818
58.5%
. 8191
32.3%
5 1072
 
4.2%
9 757
 
3.0%
3 492
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14818
58.5%
. 8191
32.3%
5 1072
 
4.2%
9 757
 
3.0%
3 492
 
1.9%

oh_DeliveryReasonCode
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing82254
Missing (%)> 99.9%
Memory size642.7 KiB
2023-11-24T14:24:24.447570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowLD L
ValueCountFrequency (%)
ld 1
50.0%
l 1
50.0%
2023-11-24T14:24:24.676529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2
50.0%
D 1
25.0%
1
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3
75.0%
Space Separator 1
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 2
66.7%
D 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3
75.0%
Common 1
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 2
66.7%
D 1
33.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 2
50.0%
D 1
25.0%
1
25.0%

oh_DiscountValue
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct113
Distinct (%)1.4%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean3.0475534
Minimum0
Maximum175.05
Zeros7411
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:24.817247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.85
Maximum175.05
Range175.05
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.204934
Coefficient of variation (CV)4.6610943
Kurtosis74.936634
Mean3.0475534
Median Absolute Deviation (MAD)0
Skewness7.7088354
Sum24962.51
Variance201.78014
MonotonicityNot monotonic
2023-11-24T14:24:24.963231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7411
 
9.0%
25 52
 
0.1%
9.75 28
 
< 0.1%
175.05 25
 
< 0.1%
40 25
 
< 0.1%
35 22
 
< 0.1%
28 19
 
< 0.1%
15 18
 
< 0.1%
86.3 16
 
< 0.1%
10 15
 
< 0.1%
Other values (103) 560
 
0.7%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 7411
9.0%
1.69 5
 
< 0.1%
1.75 3
 
< 0.1%
1.92 4
 
< 0.1%
2.21 2
 
< 0.1%
2.7 5
 
< 0.1%
2.81 4
 
< 0.1%
3.12 3
 
< 0.1%
3.56 4
 
< 0.1%
3.69 3
 
< 0.1%
ValueCountFrequency (%)
175.05 25
< 0.1%
105 9
 
< 0.1%
100.6 8
 
< 0.1%
90 5
 
< 0.1%
86.3 16
< 0.1%
80.5 9
 
< 0.1%
80 5
 
< 0.1%
72.25 4
 
< 0.1%
66.8 4
 
< 0.1%
63.2 12
< 0.1%

oh_DiscountReasonCode
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing71543
Missing (%)87.0%
Memory size642.7 KiB
PGR
10712 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32136
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPGR
2nd rowPGR
3rd rowPGR
4th rowPGR
5th rowPGR

Common Values

ValueCountFrequency (%)
PGR 10712
 
13.0%
(Missing) 71543
87.0%

Length

2023-11-24T14:24:25.082544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:25.168715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pgr 10712
100.0%

Most occurring characters

ValueCountFrequency (%)
P 10712
33.3%
G 10712
33.3%
R 10712
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32136
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 10712
33.3%
G 10712
33.3%
R 10712
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 32136
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 10712
33.3%
G 10712
33.3%
R 10712
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32136
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 10712
33.3%
G 10712
33.3%
R 10712
33.3%

oh_LoyaltyValue
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:25.240092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:25.332688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

oh_OrderMethod
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
5.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:25.411056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:25.520760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
5 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8191
50.0%
0 8191
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 8191
33.3%
. 8191
33.3%
0 8191
33.3%

oh_OrderType
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
8114 
2.0
 
77

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8114
 
9.9%
2.0 77
 
0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:25.611081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:25.725285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8114
99.1%
2.0 77
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 16305
66.4%
. 8191
33.3%
2 77
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16305
99.5%
2 77
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16305
66.4%
. 8191
33.3%
2 77
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16305
66.4%
. 8191
33.3%
2 77
 
0.3%

oh_OrderMajorStatus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
Despatch Confirmed
65082 
Ready For Despatch
14518 
Partialy Despatched
 
2124
Partialy Held
 
238
Picking at Store
 
166
Other values (2)
 
126

Length

Max length19
Median length18
Mean length17.994237
Min length9

Characters and Unicode

Total characters1480098
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDespatch Confirmed
2nd rowDespatch Confirmed
3rd rowDespatch Confirmed
4th rowDespatch Confirmed
5th rowDespatch Confirmed

Common Values

ValueCountFrequency (%)
Despatch Confirmed 65082
79.1%
Ready For Despatch 14518
 
17.6%
Partialy Despatched 2124
 
2.6%
Partialy Held 238
 
0.3%
Picking at Store 166
 
0.2%
Cancelled 68
 
0.1%
Held Order 58
 
0.1%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:25.825009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:26.008858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
despatch 79600
44.4%
confirmed 65082
36.3%
ready 14518
 
8.1%
for 14518
 
8.1%
partialy 2362
 
1.3%
despatched 2124
 
1.2%
held 296
 
0.2%
picking 166
 
0.1%
at 166
 
0.1%
store 166
 
0.1%
Other values (2) 126
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 164104
 
11.1%
a 101200
 
6.8%
96870
 
6.5%
t 84418
 
5.7%
r 82244
 
5.6%
d 82146
 
5.6%
c 81958
 
5.5%
D 81724
 
5.5%
s 81724
 
5.5%
p 81724
 
5.5%
Other values (17) 541986
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1204270
81.4%
Uppercase Letter 178958
 
12.1%
Space Separator 96870
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 164104
13.6%
a 101200
 
8.4%
t 84418
 
7.0%
r 82244
 
6.8%
d 82146
 
6.8%
c 81958
 
6.8%
s 81724
 
6.8%
p 81724
 
6.8%
h 81724
 
6.8%
o 79766
 
6.6%
Other values (8) 283262
23.5%
Uppercase Letter
ValueCountFrequency (%)
D 81724
45.7%
C 65150
36.4%
R 14518
 
8.1%
F 14518
 
8.1%
P 2528
 
1.4%
H 296
 
0.2%
S 166
 
0.1%
O 58
 
< 0.1%
Space Separator
ValueCountFrequency (%)
96870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1383228
93.5%
Common 96870
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 164104
 
11.9%
a 101200
 
7.3%
t 84418
 
6.1%
r 82244
 
5.9%
d 82146
 
5.9%
c 81958
 
5.9%
D 81724
 
5.9%
s 81724
 
5.9%
p 81724
 
5.9%
h 81724
 
5.9%
Other values (16) 460262
33.3%
Common
ValueCountFrequency (%)
96870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1480098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 164104
 
11.1%
a 101200
 
6.8%
96870
 
6.5%
t 84418
 
5.7%
r 82244
 
5.6%
d 82146
 
5.6%
c 81958
 
5.5%
D 81724
 
5.5%
s 81724
 
5.5%
p 81724
 
5.5%
Other values (17) 541986
36.6%

oh_OrderMinorStatus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
None
81878 
Stock Control
 
358
Duplicate Order
 
18

Length

Max length15
Median length4
Mean length4.0415785
Min length4

Characters and Unicode

Total characters332436
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 81878
99.5%
Stock Control 358
 
0.4%
Duplicate Order 18
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:26.119147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:26.231139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
none 81878
99.1%
stock 358
 
0.4%
control 358
 
0.4%
duplicate 18
 
< 0.1%
order 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 82952
25.0%
n 82236
24.7%
e 81914
24.6%
N 81878
24.6%
t 734
 
0.2%
r 394
 
0.1%
l 376
 
0.1%
c 376
 
0.1%
376
 
0.1%
C 358
 
0.1%
Other values (9) 842
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249430
75.0%
Uppercase Letter 82630
 
24.9%
Space Separator 376
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 82952
33.3%
n 82236
33.0%
e 81914
32.8%
t 734
 
0.3%
r 394
 
0.2%
l 376
 
0.2%
c 376
 
0.2%
k 358
 
0.1%
u 18
 
< 0.1%
p 18
 
< 0.1%
Other values (3) 54
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 81878
99.1%
C 358
 
0.4%
S 358
 
0.4%
D 18
 
< 0.1%
O 18
 
< 0.1%
Space Separator
ValueCountFrequency (%)
376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332060
99.9%
Common 376
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 82952
25.0%
n 82236
24.8%
e 81914
24.7%
N 81878
24.7%
t 734
 
0.2%
r 394
 
0.1%
l 376
 
0.1%
c 376
 
0.1%
C 358
 
0.1%
k 358
 
0.1%
Other values (8) 484
 
0.1%
Common
ValueCountFrequency (%)
376
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 82952
25.0%
n 82236
24.7%
e 81914
24.6%
N 81878
24.6%
t 734
 
0.2%
r 394
 
0.1%
l 376
 
0.1%
c 376
 
0.1%
376
 
0.1%
C 358
 
0.1%
Other values (9) 842
 
0.3%
Distinct22297
Distinct (%)27.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
Minimum2022-10-12 11:22:12
Maximum2023-11-23 17:15:01
2023-11-24T14:24:26.336262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:26.665149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

oh_DespatchDate
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

oh_CancelledDate
Date

MISSING 

Distinct12
Distinct (%)30.8%
Missing82216
Missing (%)> 99.9%
Memory size642.7 KiB
Minimum2023-11-21 17:11:06.440000
Maximum2023-11-23 15:55:19.517000
2023-11-24T14:24:26.811888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:26.915339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

oh_Priority
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:27.024987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:27.126504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

oh_UserID
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
1000.0
8191 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters49146
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000.0
2nd row1000.0
3rd row1000.0
4th row1000.0
5th row1000.0

Common Values

ValueCountFrequency (%)
1000.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:27.221792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:27.332086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1000.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40955
83.3%
Other Punctuation 8191
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32764
80.0%
1 8191
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

oh_CurrencyCode
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing3734
Missing (%)4.5%
Memory size642.7 KiB
GBP
78521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters235563
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGBP
2nd rowGBP
3rd rowGBP
4th rowGBP
5th rowGBP

Common Values

ValueCountFrequency (%)
GBP 78521
95.5%
(Missing) 3734
 
4.5%

Length

2023-11-24T14:24:27.416809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:27.541431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
gbp 78521
100.0%

Most occurring characters

ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 235563
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 235563
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

oh_CurrencyRate
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
1.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:27.639667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:27.749510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8191
50.0%
0 8191
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

oh_LastActionCode
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)0.2%
Missing79234
Missing (%)96.3%
Memory size642.7 KiB
CIR
2786 
COA
 
85
RFI
 
67
CANI
 
46
CICR
 
14
Other values (2)
 
23

Length

Max length4
Median length3
Mean length3.01953
Min length2

Characters and Unicode

Total characters9122
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIR
2nd rowCIR
3rd rowCIR
4th rowCIR
5th rowCIR

Common Values

ValueCountFrequency (%)
CIR 2786
 
3.4%
COA 85
 
0.1%
RFI 67
 
0.1%
CANI 46
 
0.1%
CICR 14
 
< 0.1%
CI 12
 
< 0.1%
COCR 11
 
< 0.1%
(Missing) 79234
96.3%

Length

2023-11-24T14:24:27.860772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:28.027484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
cir 2786
92.2%
coa 85
 
2.8%
rfi 67
 
2.2%
cani 46
 
1.5%
cicr 14
 
0.5%
ci 12
 
0.4%
cocr 11
 
0.4%

Most occurring characters

ValueCountFrequency (%)
C 2979
32.7%
I 2925
32.1%
R 2878
31.6%
A 131
 
1.4%
O 96
 
1.1%
F 67
 
0.7%
N 46
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9122
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2979
32.7%
I 2925
32.1%
R 2878
31.6%
A 131
 
1.4%
O 96
 
1.1%
F 67
 
0.7%
N 46
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 9122
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2979
32.7%
I 2925
32.1%
R 2878
31.6%
A 131
 
1.4%
O 96
 
1.1%
F 67
 
0.7%
N 46
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2979
32.7%
I 2925
32.1%
R 2878
31.6%
A 131
 
1.4%
O 96
 
1.1%
F 67
 
0.7%
N 46
 
0.5%

oh_DueDate
Date

MISSING 

Distinct20046
Distinct (%)25.6%
Missing3969
Missing (%)4.8%
Memory size642.7 KiB
Minimum2022-10-12 11:22:12
Maximum2023-11-23 17:15:01
2023-11-24T14:24:28.172629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:28.357581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

oh_DeliverByDate
Date

MISSING 

Distinct20046
Distinct (%)25.6%
Missing3969
Missing (%)4.8%
Memory size642.7 KiB
Minimum2022-10-12 11:22:12
Maximum2023-11-23 17:15:01
2023-11-24T14:24:28.530062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:28.687608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

oh_ShippingCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size642.7 KiB
RMT
49311 
CCHERMSTD
17949 
CLCC
7538 
CCJLP
 
3726
CCDPDND
 
1902
Other values (7)
 
1824

Length

Max length9
Median length3
Mean length4.6685957
Min length3

Characters and Unicode

Total characters383992
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRMT
2nd rowRMT
3rd rowRMT
4th rowRMT
5th rowCCDPDND

Common Values

ValueCountFrequency (%)
RMT 49311
59.9%
CCHERMSTD 17949
 
21.8%
CLCC 7538
 
9.2%
CCJLP 3726
 
4.5%
CCDPDND 1902
 
2.3%
CCHERMPS 1092
 
1.3%
GSTD 213
 
0.3%
CCVRY 163
 
0.2%
RMGIFT 106
 
0.1%
GEXP 95
 
0.1%
Other values (2) 155
 
0.2%

Length

2023-11-24T14:24:28.837524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rmt 49311
60.0%
cchermstd 17949
 
21.8%
clcc 7538
 
9.2%
ccjlp 3726
 
4.5%
ccdpdnd 1902
 
2.3%
cchermps 1092
 
1.3%
gstd 213
 
0.3%
ccvry 163
 
0.2%
rmgift 106
 
0.1%
gexp 95
 
0.1%
Other values (2) 155
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 72430
18.9%
R 68700
17.9%
M 68537
17.8%
T 67734
17.6%
D 23868
 
6.2%
S 19254
 
5.0%
E 19212
 
5.0%
H 19041
 
5.0%
L 11264
 
2.9%
P 6967
 
1.8%
Other values (10) 6985
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 383834
> 99.9%
Decimal Number 158
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 72430
18.9%
R 68700
17.9%
M 68537
17.9%
T 67734
17.6%
D 23868
 
6.2%
S 19254
 
5.0%
E 19212
 
5.0%
H 19041
 
5.0%
L 11264
 
2.9%
P 6967
 
1.8%
Other values (8) 6827
 
1.8%
Decimal Number
ValueCountFrequency (%)
2 79
50.0%
4 79
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 383834
> 99.9%
Common 158
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 72430
18.9%
R 68700
17.9%
M 68537
17.9%
T 67734
17.6%
D 23868
 
6.2%
S 19254
 
5.0%
E 19212
 
5.0%
H 19041
 
5.0%
L 11264
 
2.9%
P 6967
 
1.8%
Other values (8) 6827
 
1.8%
Common
ValueCountFrequency (%)
2 79
50.0%
4 79
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 383992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 72430
18.9%
R 68700
17.9%
M 68537
17.8%
T 67734
17.6%
D 23868
 
6.2%
S 19254
 
5.0%
E 19212
 
5.0%
H 19041
 
5.0%
L 11264
 
2.9%
P 6967
 
1.8%
Other values (10) 6985
 
1.8%

oh_ShippingReasonCode
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing82254
Missing (%)> 99.9%
Memory size642.7 KiB
2023-11-24T14:24:28.968015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowLD L
ValueCountFrequency (%)
ld 1
50.0%
l 1
50.0%
2023-11-24T14:24:29.169775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 2
50.0%
D 1
25.0%
1
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3
75.0%
Space Separator 1
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 2
66.7%
D 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3
75.0%
Common 1
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 2
66.7%
D 1
33.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 2
50.0%
D 1
25.0%
1
25.0%

oh_PII
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
****
82254 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters329016
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row****
2nd row****
3rd row****
4th row****
5th row****

Common Values

ValueCountFrequency (%)
**** 82254
> 99.9%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:29.276964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:29.375319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
82254
100.0%

Most occurring characters

ValueCountFrequency (%)
* 329016
100.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 329016
100.0%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
* 329016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 329016
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 329016
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 329016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 329016
100.0%

ol_ClientCode
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
CRW
82254 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters246762
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCRW
2nd rowCRW
3rd rowCRW
4th rowCRW
5th rowCRW

Common Values

ValueCountFrequency (%)
CRW 82254
> 99.9%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:29.472771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:29.594243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
crw 82254
100.0%

Most occurring characters

ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 246762
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 246762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 82254
33.3%
R 82254
33.3%
W 82254
33.3%

ol_OrderID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2113
Distinct (%)25.8%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean6519444.4
Minimum5421459
Maximum6555985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:29.754965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5421459
5-th percentile6471496
Q16512281.5
median6525779
Q36537162
95-th percentile6550271.5
Maximum6555985
Range1134526
Interquartile range (IQR)24880.5

Descriptive statistics

Standard deviation41613.862
Coefficient of variation (CV)0.0063830381
Kurtosis191.72675
Mean6519444.4
Median Absolute Deviation (MAD)12142
Skewness-10.705328
Sum5.3400769 × 1010
Variance1.7317135 × 109
MonotonicityNot monotonic
2023-11-24T14:24:30.066761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6514411 25
 
< 0.1%
6489908 19
 
< 0.1%
6552899 18
 
< 0.1%
6541399 17
 
< 0.1%
6474533 16
 
< 0.1%
6533665 15
 
< 0.1%
6522805 15
 
< 0.1%
6533159 14
 
< 0.1%
6482742 14
 
< 0.1%
6542958 14
 
< 0.1%
Other values (2103) 8024
 
9.8%
(Missing) 74064
90.0%
ValueCountFrequency (%)
5421459 2
 
< 0.1%
5864333 2
 
< 0.1%
5942595 4
< 0.1%
5983878 7
< 0.1%
6048975 4
< 0.1%
6081160 4
< 0.1%
6111623 4
< 0.1%
6334792 2
 
< 0.1%
6344916 4
< 0.1%
6345788 3
< 0.1%
ValueCountFrequency (%)
6555985 1
 
< 0.1%
6555965 9
< 0.1%
6555919 2
 
< 0.1%
6555892 9
< 0.1%
6555809 2
 
< 0.1%
6555777 3
 
< 0.1%
6555770 3
 
< 0.1%
6555726 2
 
< 0.1%
6555724 2
 
< 0.1%
6555650 6
< 0.1%
Distinct23772
Distinct (%)29.0%
Missing339
Missing (%)0.4%
Memory size642.7 KiB
2023-11-24T14:24:30.418488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length16
Median length8
Mean length8.1055471
Min length5

Characters and Unicode

Total characters663974
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)3.0%

Sample

1st row12841488
2nd row12841488
3rd row13158853
4th row13158853
5th row13217192
ValueCountFrequency (%)
13715808 32
 
< 0.1%
13660449 25
 
< 0.1%
13719960 24
 
< 0.1%
13710909 23
 
< 0.1%
13707110 23
 
< 0.1%
13666197 21
 
< 0.1%
13714759 20
 
< 0.1%
13719757 20
 
< 0.1%
13708042 20
 
< 0.1%
13704457 20
 
< 0.1%
Other values (23762) 81688
99.7%
2023-11-24T14:24:31.024325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 147525
22.2%
3 119543
18.0%
7 103507
15.6%
0 69125
10.4%
6 44337
 
6.7%
2 36135
 
5.4%
8 35524
 
5.4%
9 35514
 
5.3%
4 33863
 
5.1%
5 33859
 
5.1%
Other values (9) 5042
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658932
99.2%
Uppercase Letter 4716
 
0.7%
Dash Punctuation 326
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147525
22.4%
3 119543
18.1%
7 103507
15.7%
0 69125
10.5%
6 44337
 
6.7%
2 36135
 
5.5%
8 35524
 
5.4%
9 35514
 
5.4%
4 33863
 
5.1%
5 33859
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
Y 3889
82.5%
V 163
 
3.5%
E 163
 
3.5%
R 163
 
3.5%
O 163
 
3.5%
S 114
 
2.4%
M 49
 
1.0%
X 12
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 326
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 659258
99.3%
Latin 4716
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 147525
22.4%
3 119543
18.1%
7 103507
15.7%
0 69125
10.5%
6 44337
 
6.7%
2 36135
 
5.5%
8 35524
 
5.4%
9 35514
 
5.4%
4 33863
 
5.1%
5 33859
 
5.1%
Latin
ValueCountFrequency (%)
Y 3889
82.5%
V 163
 
3.5%
E 163
 
3.5%
R 163
 
3.5%
O 163
 
3.5%
S 114
 
2.4%
M 49
 
1.0%
X 12
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 147525
22.2%
3 119543
18.0%
7 103507
15.6%
0 69125
10.4%
6 44337
 
6.7%
2 36135
 
5.4%
8 35524
 
5.4%
9 35514
 
5.3%
4 33863
 
5.1%
5 33859
 
5.1%
Other values (9) 5042
 
0.8%

ol_CustomerID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2037
Distinct (%)24.9%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean24851252
Minimum10512
Maximum34101675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:31.176388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10512
5-th percentile9959870
Q117903003
median25847047
Q333849878
95-th percentile34033050
Maximum34101675
Range34091163
Interquartile range (IQR)15946875

Descriptive statistics

Standard deviation8493903.9
Coefficient of variation (CV)0.34178978
Kurtosis-0.9286762
Mean24851252
Median Absolute Deviation (MAD)7998564
Skewness-0.46525263
Sum2.035566 × 1011
Variance7.2146403 × 1013
MonotonicityNot monotonic
2023-11-24T14:24:31.303887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16635245 26
 
< 0.1%
10425049 25
 
< 0.1%
15614761 24
 
< 0.1%
33836149 19
 
< 0.1%
34087130 18
 
< 0.1%
17181934 17
 
< 0.1%
33912221 17
 
< 0.1%
17162728 16
 
< 0.1%
33779356 16
 
< 0.1%
34009472 15
 
< 0.1%
Other values (2027) 7998
 
9.7%
(Missing) 74064
90.0%
ValueCountFrequency (%)
10512 7
< 0.1%
494757 3
< 0.1%
691469 3
< 0.1%
989806 3
< 0.1%
1848530 3
< 0.1%
3014983 4
< 0.1%
3396223 3
< 0.1%
3767001 3
< 0.1%
3799000 6
< 0.1%
3809340 7
< 0.1%
ValueCountFrequency (%)
34101675 1
 
< 0.1%
34101451 2
 
< 0.1%
34100990 2
 
< 0.1%
34100123 6
< 0.1%
34100040 4
< 0.1%
34099788 2
 
< 0.1%
34099705 2
 
< 0.1%
34097493 3
< 0.1%
34096024 3
< 0.1%
34095323 4
< 0.1%

ol_LineID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)0.3%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean2.8921988
Minimum0
Maximum24
Zeros195
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:31.428356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3099247
Coefficient of variation (CV)0.79867427
Kurtosis11.171599
Mean2.8921988
Median Absolute Deviation (MAD)1
Skewness2.5732805
Sum23690
Variance5.3357523
MonotonicityNot monotonic
2023-11-24T14:24:31.575207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 2113
 
2.6%
2 2096
 
2.5%
3 1609
 
2.0%
4 896
 
1.1%
5 497
 
0.6%
6 267
 
0.3%
0 195
 
0.2%
7 166
 
0.2%
8 103
 
0.1%
9 69
 
0.1%
Other values (15) 180
 
0.2%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 195
 
0.2%
1 2113
2.6%
2 2096
2.5%
3 1609
2.0%
4 896
1.1%
5 497
 
0.6%
6 267
 
0.3%
7 166
 
0.2%
8 103
 
0.1%
9 69
 
0.1%
ValueCountFrequency (%)
24 2
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
20 3
 
< 0.1%
19 2
 
< 0.1%
18 5
< 0.1%
17 4
< 0.1%
16 7
< 0.1%
15 8
< 0.1%
Distinct82026
Distinct (%)99.7%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
Minimum2022-10-12 11:33:22.093000
Maximum2023-11-23 17:23:24.667000
2023-11-24T14:24:31.745360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:31.941905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ol_CampaignCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
WEBT
76301 
JLP
 
3726
WEBC
 
1084
UKMO
 
355
GLBE
 
308
Other values (6)
 
480

Length

Max length4
Median length4
Mean length3.9545919
Min length2

Characters and Unicode

Total characters325281
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWEBT
2nd rowWEBT
3rd rowWEBT
4th rowWEBT
5th rowWEBT

Common Values

ValueCountFrequency (%)
WEBT 76301
92.8%
JLP 3726
 
4.5%
WEBC 1084
 
1.3%
UKMO 355
 
0.4%
GLBE 308
 
0.4%
GIFT 234
 
0.3%
VERY 163
 
0.2%
NXPP 76
 
0.1%
CAT 4
 
< 0.1%
MO 2
 
< 0.1%

Length

2023-11-24T14:24:32.103572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
webt 76301
92.8%
jlp 3726
 
4.5%
webc 1084
 
1.3%
ukmo 355
 
0.4%
glbe 308
 
0.4%
gift 234
 
0.3%
very 163
 
0.2%
nxpp 76
 
0.1%
cat 4
 
< 0.1%
mo 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 325281
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 325281
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 77856
23.9%
B 77693
23.9%
W 77385
23.8%
T 76539
23.5%
L 4034
 
1.2%
P 3878
 
1.2%
J 3726
 
1.1%
C 1088
 
0.3%
G 542
 
0.2%
O 358
 
0.1%
Other values (11) 2182
 
0.7%

ol_SourceCode
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
WEB
64871 
CLIC
7538 
AW
 
5851
JLP
 
3726
VERY
 
163
Other values (5)
 
105

Length

Max length10
Median length3
Mean length3.0245702
Min length2

Characters and Unicode

Total characters248783
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWEB
2nd rowWEB
3rd rowWEB
4th rowWEB
5th rowWEB

Common Values

ValueCountFrequency (%)
WEB 64871
78.9%
CLIC 7538
 
9.2%
AW 5851
 
7.1%
JLP 3726
 
4.5%
VERY 163
 
0.2%
NXPP 76
 
0.1%
AWSRC=AW 13
 
< 0.1%
AT01 9
 
< 0.1%
CAT 4
 
< 0.1%
MAIL ORDER 3
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:32.231771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:32.381560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
web 64871
78.9%
clic 7538
 
9.2%
aw 5851
 
7.1%
jlp 3726
 
4.5%
very 163
 
0.2%
nxpp 76
 
0.1%
awsrc=aw 13
 
< 0.1%
at01 9
 
< 0.1%
cat 4
 
< 0.1%
mail 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (13) 547
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 248749
> 99.9%
Decimal Number 18
 
< 0.1%
Math Symbol 13
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (9) 513
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 9
50.0%
1 9
50.0%
Math Symbol
ValueCountFrequency (%)
= 13
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 248749
> 99.9%
Common 34
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (9) 513
 
0.2%
Common
ValueCountFrequency (%)
= 13
38.2%
0 9
26.5%
1 9
26.5%
3
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 248783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 70748
28.4%
E 65037
26.1%
B 64871
26.1%
C 15093
 
6.1%
L 11267
 
4.5%
I 7541
 
3.0%
A 5893
 
2.4%
P 3878
 
1.6%
J 3726
 
1.5%
R 182
 
0.1%
Other values (13) 547
 
0.2%

ol_MediaID
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing82252
Missing (%)> 99.9%
Memory size642.7 KiB
2023-11-24T14:24:32.827392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length20
Median length11
Mean length14
Min length11

Characters and Unicode

Total characters42
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowPHONE Order
2nd rowPHONE Order
3rd rowVerifone PHONE Order
ValueCountFrequency (%)
phone 3
42.9%
order 3
42.9%
verifone 1
 
14.3%
2023-11-24T14:24:33.210716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 7
16.7%
O 6
14.3%
e 5
11.9%
4
9.5%
P 3
7.1%
H 3
7.1%
N 3
7.1%
E 3
7.1%
d 3
7.1%
V 1
 
2.4%
Other values (4) 4
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19
45.2%
Uppercase Letter 19
45.2%
Space Separator 4
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7
36.8%
e 5
26.3%
d 3
15.8%
i 1
 
5.3%
f 1
 
5.3%
o 1
 
5.3%
n 1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 6
31.6%
P 3
15.8%
H 3
15.8%
N 3
15.8%
E 3
15.8%
V 1
 
5.3%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38
90.5%
Common 4
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7
18.4%
O 6
15.8%
e 5
13.2%
P 3
7.9%
H 3
7.9%
N 3
7.9%
E 3
7.9%
d 3
7.9%
V 1
 
2.6%
i 1
 
2.6%
Other values (3) 3
7.9%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7
16.7%
O 6
14.3%
e 5
11.9%
4
9.5%
P 3
7.1%
H 3
7.1%
N 3
7.1%
E 3
7.1%
d 3
7.1%
V 1
 
2.4%
Other values (4) 4
9.5%
Distinct8013
Distinct (%)9.7%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
2023-11-24T14:24:33.732291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length15
Median length7
Mean length7.2566684
Min length5

Characters and Unicode

Total characters596890
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2913 ?
Unique (%)3.5%

Sample

1st row1221290
2nd rowFRL-999
3rd row1250824
4th rowFRL-999
5th row1246696
ValueCountFrequency (%)
frl-999 20660
25.1%
intro 5384
 
6.5%
system_shipping 3959
 
4.8%
1223300 479
 
0.6%
1278237 369
 
0.4%
1179890 305
 
0.4%
1167786 277
 
0.3%
1278240 268
 
0.3%
1278238 262
 
0.3%
1269082 254
 
0.3%
Other values (8003) 50037
60.8%
2023-11-24T14:24:34.354480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 81299
13.6%
1 77412
13.0%
2 67709
11.3%
7 45672
 
7.7%
8 34552
 
5.8%
3 27405
 
4.6%
R 26107
 
4.4%
6 25192
 
4.2%
4 23546
 
3.9%
5 23028
 
3.9%
Other values (21) 164968
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 427417
71.6%
Uppercase Letter 144850
 
24.3%
Dash Punctuation 20660
 
3.5%
Connector Punctuation 3963
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 26107
18.0%
F 20719
14.3%
L 20664
14.3%
I 13361
9.2%
S 11877
8.2%
T 9406
 
6.5%
N 9343
 
6.5%
P 7918
 
5.5%
O 5388
 
3.7%
G 4022
 
2.8%
Other values (9) 16045
11.1%
Decimal Number
ValueCountFrequency (%)
9 81299
19.0%
1 77412
18.1%
2 67709
15.8%
7 45672
10.7%
8 34552
8.1%
3 27405
 
6.4%
6 25192
 
5.9%
4 23546
 
5.5%
5 23028
 
5.4%
0 21602
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 20660
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 452040
75.7%
Latin 144850
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 26107
18.0%
F 20719
14.3%
L 20664
14.3%
I 13361
9.2%
S 11877
8.2%
T 9406
 
6.5%
N 9343
 
6.5%
P 7918
 
5.5%
O 5388
 
3.7%
G 4022
 
2.8%
Other values (9) 16045
11.1%
Common
ValueCountFrequency (%)
9 81299
18.0%
1 77412
17.1%
2 67709
15.0%
7 45672
10.1%
8 34552
7.6%
3 27405
 
6.1%
6 25192
 
5.6%
4 23546
 
5.2%
5 23028
 
5.1%
0 21602
 
4.8%
Other values (2) 24623
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 596890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 81299
13.6%
1 77412
13.0%
2 67709
11.3%
7 45672
 
7.7%
8 34552
 
5.8%
3 27405
 
4.6%
R 26107
 
4.4%
6 25192
 
4.2%
4 23546
 
3.9%
5 23028
 
3.9%
Other values (21) 164968
27.6%

ol_Quantity
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
1.0
8136 
2.0
 
48
3.0
 
4
4.0
 
2
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8136
 
9.9%
2.0 48
 
0.1%
3.0 4
 
< 0.1%
4.0 2
 
< 0.1%
5.0 1
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:34.517437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:34.640384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8136
99.3%
2.0 48
 
0.6%
3.0 4
 
< 0.1%
4.0 2
 
< 0.1%
5.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
1 8136
33.1%
2 48
 
0.2%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8191
50.0%
1 8136
49.7%
2 48
 
0.3%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
1 8136
33.1%
2 48
 
0.2%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
1 8136
33.1%
2 48
 
0.2%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%

ol_QuantityDespatched
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.1%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean1.0050055
Minimum0
Maximum5
Zeros25
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:34.740676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12244696
Coefficient of variation (CV)0.1218371
Kurtosis296.79837
Mean1.0050055
Median Absolute Deviation (MAD)0
Skewness11.385912
Sum8232
Variance0.014993257
MonotonicityNot monotonic
2023-11-24T14:24:34.840313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 8111
 
9.9%
2 48
 
0.1%
0 25
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 25
 
< 0.1%
1 8111
9.9%
2 48
 
0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 2
 
< 0.1%
3 4
 
< 0.1%
2 48
 
0.1%
1 8111
9.9%
0 25
 
< 0.1%

ol_DespatchedDate
Date

MISSING 

Distinct66189
Distinct (%)99.8%
Missing15965
Missing (%)19.4%
Memory size642.7 KiB
Minimum2022-10-13 11:29:20.203000
Maximum2023-11-23 16:53:23.680000
2023-11-24T14:24:34.968404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:35.242631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ol_LineMajorStatus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
Despatch Confirmed
62347 
Torque Processing
9571 
Ready For Despatch
 
5329
Cancelled
 
3852
Ship from Store Processing
 
563
Other values (7)
 
592

Length

Max length26
Median length18
Mean length17.472451
Min length7

Characters and Unicode

Total characters1437179
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCancelled
2nd rowDespatch Confirmed
3rd rowCancelled
4th rowDespatch Confirmed
5th rowCancelled

Common Values

ValueCountFrequency (%)
Despatch Confirmed 62347
75.8%
Torque Processing 9571
 
11.6%
Ready For Despatch 5329
 
6.5%
Cancelled 3852
 
4.7%
Ship from Store Processing 563
 
0.7%
Replaced 216
 
0.3%
Picking at Store 165
 
0.2%
Stock Control 95
 
0.1%
Torque Queued 70
 
0.1%
Swapped 23
 
< 0.1%
Other values (2) 23
 
< 0.1%

Length

2023-11-24T14:24:35.407856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
despatch 67676
40.5%
confirmed 62347
37.3%
processing 10134
 
6.1%
torque 9641
 
5.8%
for 5334
 
3.2%
ready 5329
 
3.2%
cancelled 3852
 
2.3%
store 728
 
0.4%
ship 563
 
0.3%
from 563
 
0.3%
Other values (11) 875
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 164190
 
11.4%
o 89073
 
6.2%
r 88860
 
6.2%
s 87980
 
6.1%
84788
 
5.9%
c 82161
 
5.7%
a 77266
 
5.4%
n 76603
 
5.3%
i 73389
 
5.1%
d 71855
 
5.0%
Other values (23) 541014
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1186077
82.5%
Uppercase Letter 166314
 
11.6%
Space Separator 84788
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 164190
13.8%
o 89073
 
7.5%
r 88860
 
7.5%
s 87980
 
7.4%
c 82161
 
6.9%
a 77266
 
6.5%
n 76603
 
6.5%
i 73389
 
6.2%
d 71855
 
6.1%
t 68787
 
5.8%
Other values (11) 305913
25.8%
Uppercase Letter
ValueCountFrequency (%)
D 67676
40.7%
C 66294
39.9%
P 10317
 
6.2%
T 9641
 
5.8%
R 5545
 
3.3%
F 5334
 
3.2%
S 1409
 
0.8%
Q 70
 
< 0.1%
N 18
 
< 0.1%
W 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
84788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1352391
94.1%
Common 84788
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 164190
 
12.1%
o 89073
 
6.6%
r 88860
 
6.6%
s 87980
 
6.5%
c 82161
 
6.1%
a 77266
 
5.7%
n 76603
 
5.7%
i 73389
 
5.4%
d 71855
 
5.3%
t 68787
 
5.1%
Other values (22) 472227
34.9%
Common
ValueCountFrequency (%)
84788
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437179
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 164190
 
11.4%
o 89073
 
6.2%
r 88860
 
6.2%
s 87980
 
6.1%
84788
 
5.9%
c 82161
 
5.7%
a 77266
 
5.4%
n 76603
 
5.3%
i 73389
 
5.1%
d 71855
 
5.0%
Other values (23) 541014
37.6%

ol_LineMinorStatus
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct12
Distinct (%)< 0.1%
Missing26934
Missing (%)32.7%
Memory size642.7 KiB
Charge Confirmed
37377 
Allocated
5653 
Waiting For Other Tender
4613 
Refund Confirmed
 
3678
Picked
 
1890
Other values (7)
 
2110

Length

Max length24
Median length16
Mean length15.258762
Min length6

Characters and Unicode

Total characters844130
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRefund Confirmed
2nd rowRefund Confirmed
3rd rowFailed Payment Process
4th rowCharge Confirmed
5th rowRefund Confirmed

Common Values

ValueCountFrequency (%)
Charge Confirmed 37377
45.4%
Allocated 5653
 
6.9%
Waiting For Other Tender 4613
 
5.6%
Refund Confirmed 3678
 
4.5%
Picked 1890
 
2.3%
Packed 1527
 
1.9%
Complete 447
 
0.5%
Item TOS 55
 
0.1%
In Progress 40
 
< 0.1%
Waiting For TOS Item 23
 
< 0.1%
Other values (2) 18
 
< 0.1%
(Missing) 26934
32.7%

Length

2023-11-24T14:24:35.557342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
confirmed 41055
37.2%
charge 37377
33.9%
allocated 5653
 
5.1%
waiting 4636
 
4.2%
for 4636
 
4.2%
other 4613
 
4.2%
tender 4613
 
4.2%
refund 3678
 
3.3%
picked 1890
 
1.7%
packed 1527
 
1.4%
Other values (9) 737
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 106068
12.6%
r 92375
10.9%
C 78879
 
9.3%
d 58434
 
6.9%
55094
 
6.5%
n 54023
 
6.4%
i 52218
 
6.2%
o 51832
 
6.1%
a 49195
 
5.8%
f 44733
 
5.3%
Other values (21) 201279
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 678431
80.4%
Uppercase Letter 110605
 
13.1%
Space Separator 55094
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 106068
15.6%
r 92375
13.6%
d 58434
8.6%
n 54023
8.0%
i 52218
7.7%
o 51832
7.6%
a 49195
7.3%
f 44733
6.6%
g 42053
 
6.2%
h 41990
 
6.2%
Other values (9) 85510
12.6%
Uppercase Letter
ValueCountFrequency (%)
C 78879
71.3%
A 5653
 
5.1%
O 4708
 
4.3%
T 4708
 
4.3%
F 4637
 
4.2%
W 4636
 
4.2%
R 3678
 
3.3%
P 3459
 
3.1%
I 135
 
0.1%
S 95
 
0.1%
Space Separator
ValueCountFrequency (%)
55094
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 789036
93.5%
Common 55094
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 106068
13.4%
r 92375
11.7%
C 78879
10.0%
d 58434
 
7.4%
n 54023
 
6.8%
i 52218
 
6.6%
o 51832
 
6.6%
a 49195
 
6.2%
f 44733
 
5.7%
g 42053
 
5.3%
Other values (20) 159226
20.2%
Common
ValueCountFrequency (%)
55094
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 106068
12.6%
r 92375
10.9%
C 78879
 
9.3%
d 58434
 
6.9%
55094
 
6.5%
n 54023
 
6.4%
i 52218
 
6.2%
o 51832
 
6.1%
a 49195
 
5.8%
f 44733
 
5.3%
Other values (21) 201279
23.8%

ol_SkuStatus
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
10.0
5418 
9999.0
2772 
0.0
 
1

Length

Max length6
Median length4
Mean length4.6767183
Min length3

Characters and Unicode

Total characters38307
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10.0
2nd row9999.0
3rd row10.0
4th row9999.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 5418
 
6.6%
9999.0 2772
 
3.4%
0.0 1
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:35.724312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:35.863851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
10.0 5418
66.1%
9999.0 2772
33.8%
0.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13610
35.5%
9 11088
28.9%
. 8191
21.4%
1 5418
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30116
78.6%
Other Punctuation 8191
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13610
45.2%
9 11088
36.8%
1 5418
 
18.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38307
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13610
35.5%
9 11088
28.9%
. 8191
21.4%
1 5418
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13610
35.5%
9 11088
28.9%
. 8191
21.4%
1 5418
 
14.1%

ol_SkuStockStatus
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
10.0
4521 
0.0
3662 
50.0
 
8

Length

Max length4
Median length4
Mean length3.5529239
Min length3

Characters and Unicode

Total characters29102
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row10.0
3rd row0.0
4th row10.0
5th row0.0

Common Values

ValueCountFrequency (%)
10.0 4521
 
5.5%
0.0 3662
 
4.5%
50.0 8
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:35.976196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:36.086170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
10.0 4521
55.2%
0.0 3662
44.7%
50.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 16382
56.3%
. 8191
28.1%
1 4521
 
15.5%
5 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20911
71.9%
Other Punctuation 8191
 
28.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
78.3%
1 4521
 
21.6%
5 8
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29102
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
56.3%
. 8191
28.1%
1 4521
 
15.5%
5 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
56.3%
. 8191
28.1%
1 4521
 
15.5%
5 8
 
< 0.1%

ol_PaymentMajorStatus
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing30576
Missing (%)37.2%
Memory size642.7 KiB
Charged
37878 
Authed
10122 
Refunded
 
3678
FailedRefund
 
1

Length

Max length12
Median length7
Mean length6.8754039
Min length6

Characters and Unicode

Total characters355314
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRefunded
2nd rowRefunded
3rd rowFailedRefund
4th rowCharged
5th rowCharged

Common Values

ValueCountFrequency (%)
Charged 37878
46.0%
Authed 10122
 
12.3%
Refunded 3678
 
4.5%
FailedRefund 1
 
< 0.1%
(Missing) 30576
37.2%

Length

2023-11-24T14:24:36.184282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:36.290581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
charged 37878
73.3%
authed 10122
 
19.6%
refunded 3678
 
7.1%
failedrefund 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 55358
15.6%
d 55358
15.6%
h 48000
13.5%
a 37879
10.7%
C 37878
10.7%
r 37878
10.7%
g 37878
10.7%
u 13801
 
3.9%
A 10122
 
2.8%
t 10122
 
2.8%
Other values (6) 11040
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 303634
85.5%
Uppercase Letter 51680
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 55358
18.2%
d 55358
18.2%
h 48000
15.8%
a 37879
12.5%
r 37878
12.5%
g 37878
12.5%
u 13801
 
4.5%
t 10122
 
3.3%
f 3679
 
1.2%
n 3679
 
1.2%
Other values (2) 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 37878
73.3%
A 10122
 
19.6%
R 3679
 
7.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 355314
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 55358
15.6%
d 55358
15.6%
h 48000
13.5%
a 37879
10.7%
C 37878
10.7%
r 37878
10.7%
g 37878
10.7%
u 13801
 
3.9%
A 10122
 
2.8%
t 10122
 
2.8%
Other values (6) 11040
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 55358
15.6%
d 55358
15.6%
h 48000
13.5%
a 37879
10.7%
C 37878
10.7%
r 37878
10.7%
g 37878
10.7%
u 13801
 
3.9%
A 10122
 
2.8%
t 10122
 
2.8%
Other values (6) 11040
 
3.1%

ol_PaymentMinorStatus
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing30577
Missing (%)37.2%
Memory size642.7 KiB
Complete
51678 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters413424
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowComplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete 51678
62.8%
(Missing) 30577
37.2%

Length

2023-11-24T14:24:36.379152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:36.500661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
complete 51678
100.0%

Most occurring characters

ValueCountFrequency (%)
e 103356
25.0%
C 51678
12.5%
o 51678
12.5%
m 51678
12.5%
p 51678
12.5%
l 51678
12.5%
t 51678
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 361746
87.5%
Uppercase Letter 51678
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 103356
28.6%
o 51678
14.3%
m 51678
14.3%
p 51678
14.3%
l 51678
14.3%
t 51678
14.3%
Uppercase Letter
ValueCountFrequency (%)
C 51678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 413424
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 103356
25.0%
C 51678
12.5%
o 51678
12.5%
m 51678
12.5%
p 51678
12.5%
l 51678
12.5%
t 51678
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 103356
25.0%
C 51678
12.5%
o 51678
12.5%
m 51678
12.5%
p 51678
12.5%
l 51678
12.5%
t 51678
12.5%

ol_PaymentMethod
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
2.0
8107 
4.0
 
45
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 8107
 
9.9%
4.0 45
 
0.1%
1.0 39
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:36.587321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:36.714111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 8107
99.0%
4.0 45
 
0.5%
1.0 39
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8191
50.0%
2 8107
49.5%
4 45
 
0.3%
1 39
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8191
33.3%
0 8191
33.3%
2 8107
33.0%
4 45
 
0.2%
1 39
 
0.2%

ol_PaymentType
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

ol_CancelledDate
Date

MISSING 

Distinct4119
Distinct (%)100.0%
Missing78136
Missing (%)95.0%
Memory size642.7 KiB
Minimum2023-05-11 11:42:07.973000
Maximum2023-11-23 17:25:08.793000
2023-11-24T14:24:36.830361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:36.982147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ol_LastActionCode
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.4%
Missing79709
Missing (%)96.9%
Memory size642.7 KiB
SAR
1138 
CIR
985 
RI
240 
CI
 
83
RFI
 
46
Other values (6)
 
54

Length

Max length4
Median length3
Mean length2.8762765
Min length2

Characters and Unicode

Total characters7323
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowRFI
2nd rowSAR
3rd rowRFI
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SAR 1138
 
1.4%
CIR 985
 
1.2%
RI 240
 
0.3%
CI 83
 
0.1%
RFI 46
 
0.1%
SI 22
 
< 0.1%
CANI 17
 
< 0.1%
CICR 7
 
< 0.1%
EXUN 6
 
< 0.1%
SIR 1
 
< 0.1%
(Missing) 79709
96.9%

Length

2023-11-24T14:24:37.136637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sar 1138
44.7%
cir 985
38.7%
ri 240
 
9.4%
ci 83
 
3.3%
rfi 46
 
1.8%
si 22
 
0.9%
cani 17
 
0.7%
cicr 7
 
0.3%
exun 6
 
0.2%
sir 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R 2417
33.0%
I 1402
19.1%
S 1161
15.9%
A 1155
15.8%
C 1100
15.0%
F 46
 
0.6%
N 23
 
0.3%
E 6
 
0.1%
X 6
 
0.1%
U 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7323
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 2417
33.0%
I 1402
19.1%
S 1161
15.9%
A 1155
15.8%
C 1100
15.0%
F 46
 
0.6%
N 23
 
0.3%
E 6
 
0.1%
X 6
 
0.1%
U 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7323
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 2417
33.0%
I 1402
19.1%
S 1161
15.9%
A 1155
15.8%
C 1100
15.0%
F 46
 
0.6%
N 23
 
0.3%
E 6
 
0.1%
X 6
 
0.1%
U 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 2417
33.0%
I 1402
19.1%
S 1161
15.9%
A 1155
15.8%
C 1100
15.0%
F 46
 
0.6%
N 23
 
0.3%
E 6
 
0.1%
X 6
 
0.1%
U 6
 
0.1%

ol_ParentItemNumber
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:37.241108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:37.333422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

ol_Priority
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:37.411633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:37.525742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
66.7%
. 8191
33.3%

ol_UserID
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
1000.0
8191 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters49146
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000.0
2nd row1000.0
3rd row1000.0
4th row1000.0
5th row1000.0

Common Values

ValueCountFrequency (%)
1000.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:37.618620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:37.731163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1000.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40955
83.3%
Other Punctuation 8191
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32764
80.0%
1 8191
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32764
66.7%
1 8191
 
16.7%
. 8191
 
16.7%

ol_CurrencyCode
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing3734
Missing (%)4.5%
Memory size642.7 KiB
GBP
78521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters235563
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGBP
2nd rowGBP
3rd rowGBP
4th rowGBP
5th rowGBP

Common Values

ValueCountFrequency (%)
GBP 78521
95.5%
(Missing) 3734
 
4.5%

Length

2023-11-24T14:24:37.827634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:37.938010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
gbp 78521
100.0%

Most occurring characters

ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 235563
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 235563
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 78521
33.3%
B 78521
33.3%
P 78521
33.3%

ol_CurrencyRate
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
1.0
8191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8191
 
10.0%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:38.028500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:38.128150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8191
100.0%

Most occurring characters

ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16382
66.7%
Other Punctuation 8191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8191
50.0%
0 8191
50.0%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8191
33.3%
. 8191
33.3%
0 8191
33.3%

ol_ShippingCode
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)1.5%
Missing81996
Missing (%)99.7%
Memory size642.7 KiB
CCDPDND
134 
RMT24
64 
GC
59 
RMGIFT
 
2

Length

Max length7
Median length7
Mean length5.3590734
Min length2

Characters and Unicode

Total characters1388
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRMT24
2nd rowRMT24
3rd rowRMT24
4th rowRMT24
5th rowCCDPDND

Common Values

ValueCountFrequency (%)
CCDPDND 134
 
0.2%
RMT24 64
 
0.1%
GC 59
 
0.1%
RMGIFT 2
 
< 0.1%
(Missing) 81996
99.7%

Length

2023-11-24T14:24:38.218746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:38.473102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ccdpdnd 134
51.7%
rmt24 64
24.7%
gc 59
22.8%
rmgift 2
 
0.8%

Most occurring characters

ValueCountFrequency (%)
D 402
29.0%
C 327
23.6%
P 134
 
9.7%
N 134
 
9.7%
R 66
 
4.8%
M 66
 
4.8%
T 66
 
4.8%
2 64
 
4.6%
4 64
 
4.6%
G 61
 
4.4%
Other values (2) 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1260
90.8%
Decimal Number 128
 
9.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 402
31.9%
C 327
26.0%
P 134
 
10.6%
N 134
 
10.6%
R 66
 
5.2%
M 66
 
5.2%
T 66
 
5.2%
G 61
 
4.8%
I 2
 
0.2%
F 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 64
50.0%
4 64
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1260
90.8%
Common 128
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 402
31.9%
C 327
26.0%
P 134
 
10.6%
N 134
 
10.6%
R 66
 
5.2%
M 66
 
5.2%
T 66
 
5.2%
G 61
 
4.8%
I 2
 
0.2%
F 2
 
0.2%
Common
ValueCountFrequency (%)
2 64
50.0%
4 64
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 402
29.0%
C 327
23.6%
P 134
 
9.7%
N 134
 
9.7%
R 66
 
4.8%
M 66
 
4.8%
T 66
 
4.8%
2 64
 
4.6%
4 64
 
4.6%
G 61
 
4.4%
Other values (2) 4
 
0.3%

ol_InvoiceNumber
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2091
Distinct (%)25.5%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean7573128.2
Minimum0
Maximum7772737
Zeros136
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:38.608497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7631945
Q17690921.5
median7709227
Q37721807
95-th percentile7737494
Maximum7772737
Range7772737
Interquartile range (IQR)30885.5

Descriptive statistics

Standard deviation985395.03
Coefficient of variation (CV)0.13011731
Kurtosis54.974022
Mean7573128.2
Median Absolute Deviation (MAD)15001
Skewness-7.5380247
Sum6.2031493 × 1010
Variance9.7100336 × 1011
MonotonicityNot monotonic
2023-11-24T14:24:38.767638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 136
 
0.2%
7694229 25
 
< 0.1%
7740783 18
 
< 0.1%
7663653 17
 
< 0.1%
7727136 16
 
< 0.1%
7718126 15
 
< 0.1%
7706764 15
 
< 0.1%
7691221 14
 
< 0.1%
7717482 14
 
< 0.1%
7650480 14
 
< 0.1%
Other values (2081) 7907
 
9.6%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 136
0.2%
6360410 2
 
< 0.1%
6888122 2
 
< 0.1%
6982897 4
 
< 0.1%
7037174 4
 
< 0.1%
7064757 2
 
< 0.1%
7114282 4
 
< 0.1%
7152944 4
 
< 0.1%
7191495 4
 
< 0.1%
7470866 1
 
< 0.1%
ValueCountFrequency (%)
7772737 2
 
< 0.1%
7769224 2
 
< 0.1%
7756251 1
 
< 0.1%
7756250 1
 
< 0.1%
7743939 1
 
< 0.1%
7743913 9
< 0.1%
7743866 2
 
< 0.1%
7743828 9
< 0.1%
7743739 2
 
< 0.1%
7743699 3
 
< 0.1%

ol_ItemCostPrice
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct37
Distinct (%)0.5%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean0.061087779
Minimum0
Maximum39.46
Zeros8145
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:38.916344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum39.46
Range39.46
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0075274
Coefficient of variation (CV)16.493109
Kurtosis656.11685
Mean0.061087779
Median Absolute Deviation (MAD)0
Skewness23.075982
Sum500.37
Variance1.0151115
MonotonicityNot monotonic
2023-11-24T14:24:39.051587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 8145
 
9.9%
3.83 2
 
< 0.1%
10.31 2
 
< 0.1%
4.3 2
 
< 0.1%
5.48 2
 
< 0.1%
7.17 2
 
< 0.1%
11.86 2
 
< 0.1%
9.75 2
 
< 0.1%
12.94 2
 
< 0.1%
9.86 2
 
< 0.1%
Other values (27) 28
 
< 0.1%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 8145
9.9%
2.42 1
 
< 0.1%
2.45 1
 
< 0.1%
2.76 1
 
< 0.1%
3.5 1
 
< 0.1%
3.65 1
 
< 0.1%
3.83 2
 
< 0.1%
3.98 1
 
< 0.1%
4.3 2
 
< 0.1%
5.4 1
 
< 0.1%
ValueCountFrequency (%)
39.46 1
< 0.1%
33.36 1
< 0.1%
26.61 1
< 0.1%
26.05 1
< 0.1%
25.25 1
< 0.1%
19.97 1
< 0.1%
14.95 1
< 0.1%
14.75 1
< 0.1%
13.44 1
< 0.1%
12.94 2
< 0.1%

ol_ItemUnitPrice
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct221
Distinct (%)2.7%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean30.002095
Minimum0
Maximum209.25
Zeros2774
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:39.176261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29
Q348.75
95-th percentile75
Maximum209.25
Range209.25
Interquartile range (IQR)48.75

Descriptive statistics

Standard deviation28.913988
Coefficient of variation (CV)0.96373231
Kurtosis2.3853143
Mean30.002095
Median Absolute Deviation (MAD)23.5
Skewness1.0951763
Sum245747.16
Variance836.01872
MonotonicityNot monotonic
2023-11-24T14:24:39.292331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2774
 
3.4%
44.25 580
 
0.7%
48.75 465
 
0.6%
51.75 199
 
0.2%
41.3 177
 
0.2%
56.25 168
 
0.2%
45.5 163
 
0.2%
48.3 147
 
0.2%
52.5 140
 
0.2%
18.75 140
 
0.2%
Other values (211) 3238
 
3.9%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2774
3.4%
3.6 2
 
< 0.1%
4 2
 
< 0.1%
4.5 1
 
< 0.1%
6 1
 
< 0.1%
6.3 2
 
< 0.1%
7 4
 
< 0.1%
7.2 6
 
< 0.1%
8 8
 
< 0.1%
8.1 2
 
< 0.1%
ValueCountFrequency (%)
209.25 2
 
< 0.1%
200 1
 
< 0.1%
199 2
 
< 0.1%
189 1
 
< 0.1%
171.75 2
 
< 0.1%
169 3
 
< 0.1%
168.75 11
< 0.1%
159 1
 
< 0.1%
149.25 5
< 0.1%
149 3
 
< 0.1%

ol_NetAmount
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct359
Distinct (%)4.4%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean25.088846
Minimum0
Maximum200
Zeros2579
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:39.421891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.38
Q340.63
95-th percentile61.88
Maximum200
Range200
Interquartile range (IQR)40.63

Descriptive statistics

Standard deviation23.8357
Coefficient of variation (CV)0.95005167
Kurtosis2.8107856
Mean25.088846
Median Absolute Deviation (MAD)19.37
Skewness1.1356838
Sum205502.74
Variance568.14061
MonotonicityNot monotonic
2023-11-24T14:24:39.575218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2579
 
3.1%
36.88 566
 
0.7%
40.63 450
 
0.5%
43.13 187
 
0.2%
34.42 174
 
0.2%
46.88 165
 
0.2%
37.92 161
 
0.2%
43.75 143
 
0.2%
40.25 142
 
0.2%
3.29 134
 
0.2%
Other values (349) 3490
 
4.2%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2579
3.1%
2.92 8
 
< 0.1%
3.29 134
 
0.2%
3.33 2
 
< 0.1%
3.6 2
 
< 0.1%
4.5 1
 
< 0.1%
4.96 53
 
0.1%
6 3
 
< 0.1%
6.3 2
 
< 0.1%
6.67 5
 
< 0.1%
ValueCountFrequency (%)
200 1
 
< 0.1%
189 1
 
< 0.1%
174.38 2
 
< 0.1%
165.83 1
 
< 0.1%
143.13 2
 
< 0.1%
140.83 3
 
< 0.1%
140.63 8
< 0.1%
139 2
 
< 0.1%
133.59 3
 
< 0.1%
132.67 1
 
< 0.1%

ol_TaxValue
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct283
Distinct (%)3.5%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean4.7494518
Minimum0
Maximum34.87
Zeros3037
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:39.741957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q38.12
95-th percentile12.37
Maximum34.87
Range34.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation4.8321633
Coefficient of variation (CV)1.0174149
Kurtosis2.054757
Mean4.7494518
Median Absolute Deviation (MAD)4.5
Skewness1.0774021
Sum38902.76
Variance23.349802
MonotonicityNot monotonic
2023-11-24T14:24:39.896728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3037
 
3.7%
7.37 563
 
0.7%
8.12 443
 
0.5%
8.62 188
 
0.2%
6.88 173
 
0.2%
9.37 164
 
0.2%
7.58 161
 
0.2%
8.75 143
 
0.2%
8.05 142
 
0.2%
0.66 134
 
0.2%
Other values (273) 3043
 
3.7%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 3037
3.7%
0.58 8
 
< 0.1%
0.66 134
 
0.2%
0.67 2
 
< 0.1%
0.99 53
 
0.1%
1.2 2
 
< 0.1%
1.33 5
 
< 0.1%
1.35 1
 
< 0.1%
1.42 2
 
< 0.1%
1.5 14
 
< 0.1%
ValueCountFrequency (%)
34.87 2
 
< 0.1%
33.17 1
 
< 0.1%
28.62 2
 
< 0.1%
28.17 3
 
< 0.1%
28.12 8
< 0.1%
26.72 3
 
< 0.1%
26.53 1
 
< 0.1%
26.5 1
 
< 0.1%
24.87 5
< 0.1%
24.83 2
 
< 0.1%

ol_GrossValue
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct330
Distinct (%)4.0%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean29.838269
Minimum0
Maximum209.25
Zeros2579
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:40.044628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29
Q348.75
95-th percentile74.25
Maximum209.25
Range209.25
Interquartile range (IQR)48.75

Descriptive statistics

Standard deviation28.502492
Coefficient of variation (CV)0.95523277
Kurtosis2.4825114
Mean29.838269
Median Absolute Deviation (MAD)23.05
Skewness1.1123974
Sum244405.26
Variance812.39206
MonotonicityNot monotonic
2023-11-24T14:24:40.169724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2579
 
3.1%
44.25 568
 
0.7%
48.75 453
 
0.6%
51.75 189
 
0.2%
41.3 174
 
0.2%
56.25 165
 
0.2%
45.5 161
 
0.2%
52.5 143
 
0.2%
48.3 142
 
0.2%
3.95 134
 
0.2%
Other values (320) 3483
 
4.2%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2579
3.1%
3.5 8
 
< 0.1%
3.6 2
 
< 0.1%
3.95 134
 
0.2%
4 2
 
< 0.1%
4.5 1
 
< 0.1%
5.95 53
 
0.1%
6 1
 
< 0.1%
6.3 2
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
209.25 2
 
< 0.1%
200 1
 
< 0.1%
199 1
 
< 0.1%
189 1
 
< 0.1%
171.75 2
 
< 0.1%
169 3
 
< 0.1%
168.75 8
< 0.1%
160.31 3
 
< 0.1%
159.2 1
 
< 0.1%
159 1
 
< 0.1%

ol_AmountPaid
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct340
Distinct (%)4.2%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean29.560443
Minimum0
Maximum209.25
Zeros2631
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:40.300162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28.8
Q348.75
95-th percentile74.25
Maximum209.25
Range209.25
Interquartile range (IQR)48.75

Descriptive statistics

Standard deviation28.522222
Coefficient of variation (CV)0.96487801
Kurtosis2.495342
Mean29.560443
Median Absolute Deviation (MAD)22.95
Skewness1.1236291
Sum242129.59
Variance813.51712
MonotonicityNot monotonic
2023-11-24T14:24:40.432312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2631
 
3.2%
44.25 564
 
0.7%
48.75 449
 
0.5%
51.75 188
 
0.2%
41.3 174
 
0.2%
56.25 163
 
0.2%
45.5 161
 
0.2%
48.3 142
 
0.2%
52.5 141
 
0.2%
3.95 132
 
0.2%
Other values (330) 3446
 
4.2%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 2631
3.2%
3.5 7
 
< 0.1%
3.6 2
 
< 0.1%
3.95 132
 
0.2%
4 2
 
< 0.1%
4.5 1
 
< 0.1%
5.95 52
 
0.1%
6 1
 
< 0.1%
6.3 2
 
< 0.1%
7 4
 
< 0.1%
ValueCountFrequency (%)
209.25 2
 
< 0.1%
200 1
 
< 0.1%
199 1
 
< 0.1%
189 1
 
< 0.1%
171.75 2
 
< 0.1%
169 3
 
< 0.1%
168.75 8
< 0.1%
160.31 3
 
< 0.1%
159.2 1
 
< 0.1%
159 1
 
< 0.1%

ol_AmountRefunded
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct300
Distinct (%)8.2%
Missing78577
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean48.021743
Minimum2.25
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:40.588845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.25
5-th percentile17
Q134.6025
median45.5
Q355
95-th percentile96.75
Maximum199
Range196.75
Interquartile range (IQR)20.3975

Descriptive statistics

Standard deviation23.968513
Coefficient of variation (CV)0.49911794
Kurtosis5.3500996
Mean48.021743
Median Absolute Deviation (MAD)9.8
Skewness1.7666134
Sum176623.97
Variance574.48963
MonotonicityNot monotonic
2023-11-24T14:24:40.748157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.25 391
 
0.5%
48.75 318
 
0.4%
51.75 146
 
0.2%
41.3 126
 
0.2%
48.3 124
 
0.2%
56.25 120
 
0.1%
45.5 117
 
0.1%
52.5 94
 
0.1%
18.75 71
 
0.1%
30 65
 
0.1%
Other values (290) 2106
 
2.6%
(Missing) 78577
95.5%
ValueCountFrequency (%)
2.25 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 1
 
< 0.1%
3.95 6
< 0.1%
5.95 2
 
< 0.1%
6 1
 
< 0.1%
6.3 1
 
< 0.1%
7 3
< 0.1%
7.2 1
 
< 0.1%
8 4
< 0.1%
ValueCountFrequency (%)
199 1
 
< 0.1%
171.75 2
 
< 0.1%
169 2
 
< 0.1%
168.75 10
< 0.1%
160.31 3
 
< 0.1%
159.2 1
 
< 0.1%
159 1
 
< 0.1%
157.5 1
 
< 0.1%
149.25 3
 
< 0.1%
149 2
 
< 0.1%

ol_PostageValue
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

ol_PostageTaxValue
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

ol_PostageReasonCode
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing82255
Missing (%)100.0%
Memory size642.7 KiB

ol_DiscountValue
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct126
Distinct (%)1.5%
Missing74064
Missing (%)90.0%
Infinite0
Infinite (%)0.0%
Mean0.50665609
Minimum0
Maximum42
Zeros7796
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size642.7 KiB
2023-11-24T14:24:40.905336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6119893
Coefficient of variation (CV)5.1553497
Kurtosis46.010176
Mean0.50665609
Median Absolute Deviation (MAD)0
Skewness6.2003332
Sum4150.02
Variance6.8224881
MonotonicityNot monotonic
2023-11-24T14:24:41.197247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7796
 
9.5%
8.86 35
 
< 0.1%
14 21
 
< 0.1%
9.74 19
 
< 0.1%
11.8 15
 
< 0.1%
17.5 14
 
< 0.1%
11.86 12
 
< 0.1%
10.34 11
 
< 0.1%
8.33 11
 
< 0.1%
15.8 11
 
< 0.1%
Other values (116) 246
 
0.3%
(Missing) 74064
90.0%
ValueCountFrequency (%)
0 7796
9.5%
0.46 2
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.94 7
 
< 0.1%
1.09 1
 
< 0.1%
1.12 1
 
< 0.1%
1.25 1
 
< 0.1%
1.28 1
 
< 0.1%
1.31 1
 
< 0.1%
ValueCountFrequency (%)
42 1
< 0.1%
39.8 1
< 0.1%
27.8 2
< 0.1%
27.5 2
< 0.1%
25.8 1
< 0.1%
24.11 2
< 0.1%
23.33 1
< 0.1%
22.5 1
< 0.1%
22.34 1
< 0.1%
22 2
< 0.1%

ol_DiscountReasonCode
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing81831
Missing (%)99.5%
Memory size642.7 KiB
WEB
424 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1272
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWEB
2nd rowWEB
3rd rowWEB
4th rowWEB
5th rowWEB

Common Values

ValueCountFrequency (%)
WEB 424
 
0.5%
(Missing) 81831
99.5%

Length

2023-11-24T14:24:41.302534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:41.402862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
web 424
100.0%

Most occurring characters

ValueCountFrequency (%)
W 424
33.3%
E 424
33.3%
B 424
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1272
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 424
33.3%
E 424
33.3%
B 424
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1272
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 424
33.3%
E 424
33.3%
B 424
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 424
33.3%
E 424
33.3%
B 424
33.3%

ol_TaxLocale
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing82252
Missing (%)> 99.9%
Memory size642.7 KiB
GB

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGB
2nd rowGB
3rd rowGB

Common Values

ValueCountFrequency (%)
GB 3
 
< 0.1%
(Missing) 82252
> 99.9%

Length

2023-11-24T14:24:41.501632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:41.616636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
gb 3
100.0%

Most occurring characters

ValueCountFrequency (%)
G 3
50.0%
B 3
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 3
50.0%
B 3
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 3
50.0%
B 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 3
50.0%
B 3
50.0%

ol_TaxRate
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
20.0
7731 
0.0
 
460

Length

Max length4
Median length4
Mean length3.9438408
Min length3

Characters and Unicode

Total characters32304
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row20.0
4th row20.0
5th row20.0

Common Values

ValueCountFrequency (%)
20.0 7731
 
9.4%
0.0 460
 
0.6%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:41.717260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:41.834541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
20.0 7731
94.4%
0.0 460
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 16382
50.7%
. 8191
25.4%
2 7731
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24113
74.6%
Other Punctuation 8191
 
25.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16382
67.9%
2 7731
32.1%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16382
50.7%
. 8191
25.4%
2 7731
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16382
50.7%
. 8191
25.4%
2 7731
23.9%

ol_Weight
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing74064
Missing (%)90.0%
Memory size642.7 KiB
0.0
5611 
0.01
2577 
0.1
 
3

Length

Max length4
Median length3
Mean length3.3146136
Min length3

Characters and Unicode

Total characters27150
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.01
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5611
 
6.8%
0.01 2577
 
3.1%
0.1 3
 
< 0.1%
(Missing) 74064
90.0%

Length

2023-11-24T14:24:41.935538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:42.049080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5611
68.5%
0.01 2577
31.5%
0.1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 16379
60.3%
. 8191
30.2%
1 2580
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18959
69.8%
Other Punctuation 8191
30.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16379
86.4%
1 2580
 
13.6%
Other Punctuation
ValueCountFrequency (%)
. 8191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16379
60.3%
. 8191
30.2%
1 2580
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16379
60.3%
. 8191
30.2%
1 2580
 
9.5%

ol_PII
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size642.7 KiB
****
82254 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters329016
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row****
2nd row****
3rd row****
4th row****
5th row****

Common Values

ValueCountFrequency (%)
**** 82254
> 99.9%
(Missing) 1
 
< 0.1%

Length

2023-11-24T14:24:42.135844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T14:24:42.235163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
82254
100.0%

Most occurring characters

ValueCountFrequency (%)
* 329016
100.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 329016
100.0%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
* 329016
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 329016
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 329016
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 329016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 329016
100.0%

Interactions

2023-11-24T14:24:05.283090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:02.804534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:05.879916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:08.901678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:12.004517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:14.939814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:17.719552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:20.615930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:24.017913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:27.475743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:31.104344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:34.122232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:37.123688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:39.864221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:42.669453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:45.608417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:48.490738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:51.122916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:54.128293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:56.993910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:59.691352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:02.319459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:05.385526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:02.965799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:06.021859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:09.053203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:12.119859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:15.056371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:17.869813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:20.752097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:24.150378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:27.635337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:31.214303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:34.248818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:37.228094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:39.990347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:42.800647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:45.740097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:48.612560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:51.237487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:54.237214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:57.095891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:59.815654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:02.593913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:05.509624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:03.087306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:06.138088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:09.172689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:12.248612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-11-24T14:23:30.848420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:33.844651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:36.873000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:39.609650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:42.409770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:45.306936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:48.254860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:50.883538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:53.874871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:56.733267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:59.425668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:02.098426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:05.043308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:07.945012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:05.737565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:08.741656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:11.859009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:14.813120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:17.594688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:20.486881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:23.855844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:27.354961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:30.998749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:34.009031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:37.014942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:39.741552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:42.548519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:45.458175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:48.372363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:51.021313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:54.022022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:56.867034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:23:59.569807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:02.221233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-11-24T14:24:05.161846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-11-24T14:24:42.375597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
RTYPESYS_CHANGE_OPERATIONSYS_CHANGE_VERSIONoh_CampaignCodeoh_CustomerIDoh_DeliveryGrossValueoh_DeliveryNetValueoh_DeliveryTaxValueoh_DiscountValueoh_LastActionCodeoh_NetOrderValueoh_OrderGrossValueoh_OrderIDoh_OrderMajorStatusoh_OrderMinorStatusoh_OrderTypeoh_OrderValuePaidoh_OrderValueRefundedoh_PaymentMethodoh_ShippingCodeoh_SourceCodeoh_TaxValueol_AmountPaidol_AmountRefundedol_CampaignCodeol_CustomerIDol_DiscountValueol_GrossValueol_InvoiceNumberol_ItemCostPriceol_ItemUnitPriceol_LastActionCodeol_LineIDol_LineMajorStatusol_LineMinorStatusol_NetAmountol_OrderIDol_PaymentMajorStatusol_PaymentMethodol_Quantityol_QuantityDespatchedol_ShippingCodeol_SkuStatusol_SkuStockStatusol_SourceCodeol_TaxRateol_TaxValueol_Weight
RTYPE1.0001.000NaN1.000NaN1.0001.0001.000NaN1.000NaNNaNNaN1.0001.0001.000NaNNaN1.0001.0001.000NaNNaNNaN1.000NaNNaNNaNNaNNaNNaN1.000NaN1.0001.000NaNNaN1.0001.0001.000NaN1.0001.0001.0001.0001.000NaN1.000
SYS_CHANGE_OPERATION1.0001.000NaN0.084NaN1.0001.0001.000NaN0.816NaNNaNNaN0.5020.0281.000NaN0.9501.0000.1370.130NaNNaN0.0260.084NaNNaNNaNNaNNaNNaN0.614NaN0.5130.592NaNNaN0.5441.0001.000NaN0.7761.0001.0000.1301.000NaN1.000
SYS_CHANGE_VERSIONNaNNaN1.0000.129-0.0580.0740.0650.074-0.0450.1680.0310.0290.1450.0780.0720.0560.0290.0210.1370.1280.1850.0170.0240.0260.129-0.058-0.0410.0260.152-0.0460.0230.1870.0080.1530.1470.0280.1450.1110.1370.0000.0090.6540.0000.0220.1850.0490.0170.045
oh_CampaignCode1.0000.0840.1291.000-0.0380.2170.4470.2110.0000.098-0.011-0.0080.0200.0380.0140.000-0.0090.0940.6190.7450.7460.0070.000-0.0381.000-0.0380.003-0.0210.0140.005-0.0210.1930.0150.0350.113-0.0260.0200.0260.6190.0000.0070.0660.0160.0350.7460.2140.0130.019
oh_CustomerIDNaNNaN-0.058-0.0381.0000.0830.0720.0820.0380.087-0.160-0.1610.0200.0810.0840.000-0.160-0.1110.0620.1110.057-0.174-0.129-0.0280.0551.0000.010-0.1280.026-0.019-0.1230.0590.0000.0440.022-0.1250.0200.0110.0620.000-0.0050.6990.0950.0490.0570.056-0.1390.104
oh_DeliveryGrossValue1.0001.0000.0740.2170.0831.0001.0000.998-0.0030.250-0.222-0.2220.0370.0110.0330.025-0.221-0.1820.0260.5370.064-0.214-0.099-0.0460.2170.0290.007-0.0960.044-0.019-0.1330.132-0.1840.0480.053-0.0950.0370.0500.0260.000-0.0120.0000.1280.0520.0640.000-0.0840.026
oh_DeliveryNetValue1.0001.0000.0650.4470.0721.0001.0001.000-0.0030.250-0.221-0.2220.0370.0000.0310.023-0.221-0.1820.2770.6800.063-0.214-0.099-0.0460.4470.0290.007-0.0960.044-0.019-0.1330.145-0.1840.0400.053-0.0950.0370.0500.2770.000-0.0120.0000.1290.0510.0630.072-0.0850.028
oh_DeliveryTaxValue1.0001.0000.0740.2110.0820.9981.0001.000-0.0020.250-0.225-0.2250.0370.0110.0330.025-0.224-0.1790.0190.5330.063-0.211-0.096-0.0460.2110.0290.007-0.0980.044-0.019-0.1350.130-0.1820.0480.053-0.0970.0370.0500.0190.000-0.0120.0000.1290.0520.0630.013-0.0830.027
oh_DiscountValueNaNNaN-0.0450.0000.038-0.003-0.003-0.0021.0000.0000.0900.094-0.2280.0000.0410.0000.0930.0820.0000.4130.1370.1110.0250.0500.0180.0380.7010.023-0.2250.0230.0680.1180.0760.0360.0000.021-0.2280.0060.0000.048-0.0070.1750.0180.0240.1370.0250.0340.018
oh_LastActionCode1.0000.8160.1680.0980.0870.2500.2500.2500.0001.0000.0170.0130.0370.5990.3510.0000.021-0.0360.1110.2980.191-0.083-0.020-0.0120.0980.014-0.059-0.0130.044-0.010-0.0250.5700.0120.2420.394-0.0070.0370.6400.1110.1300.0681.0000.0000.1740.1910.207-0.0430.000
oh_NetOrderValueNaNNaN0.031-0.011-0.160-0.222-0.221-0.2250.0900.0171.0000.998-0.0030.1240.2840.0800.9960.6510.0000.4190.1270.8950.2980.2080.089-0.1600.0770.3020.0140.0230.3090.1460.4410.0430.0420.303-0.0030.0500.0000.0770.0390.1470.1660.0540.1270.0560.2810.147
oh_OrderGrossValueNaNNaN0.029-0.008-0.161-0.222-0.222-0.2250.0940.0130.9981.000-0.0000.1240.2840.0780.9980.6620.0000.4190.1260.9150.3040.2220.087-0.1610.0790.3070.0170.0240.3140.1470.4360.0480.0390.306-0.0000.0470.0000.0780.0370.2670.1620.0570.1260.0340.2950.145
oh_OrderIDNaNNaN0.1450.0200.0200.0370.0370.037-0.2280.037-0.003-0.0001.0000.0950.0000.000-0.0020.0320.0000.0310.0280.0110.0610.1550.0000.020-0.1460.0650.967-0.0880.0510.154-0.0940.1320.3150.0671.0000.2580.0000.000-0.0280.3600.0000.0000.0280.0000.0640.000
oh_OrderMajorStatus1.0000.5020.0780.0380.0810.0110.0000.0110.0000.5990.1240.1240.0951.0000.7260.0000.027-0.3740.2040.0710.0630.0500.0080.0190.038-0.028-0.0180.0070.015-0.0050.0070.3620.0300.6730.6620.0060.0430.5580.2040.000-0.0940.2780.0000.2790.0630.0530.0200.000
oh_OrderMinorStatus1.0000.0280.0720.0140.0840.0330.0310.0330.0410.3510.2840.2840.0000.7261.0000.0000.0550.0060.0000.0180.0180.0910.0100.0410.014-0.0590.0230.0490.019-0.0060.0480.2270.0330.7940.5760.0480.0560.0360.0000.000-0.1710.0000.0170.3840.0180.0120.0510.017
oh_OrderType1.0001.0000.0560.0000.0000.0250.0230.0250.0000.0000.0800.0780.0000.0000.0001.000-0.120-0.0980.0000.3230.324-0.106-0.038-0.0410.0000.002-0.016-0.039-0.120-0.007-0.0380.047-0.0520.0210.019-0.0400.0610.0000.0000.000-0.0041.0000.0280.0000.3240.011-0.0340.034
oh_OrderValuePaidNaNNaN0.029-0.009-0.160-0.221-0.221-0.2240.0930.0210.9960.998-0.0020.0270.055-0.1201.0000.6640.0000.4190.1250.9120.3050.2220.088-0.1600.0790.3060.0160.0240.3130.1440.4350.0470.0380.305-0.0020.0470.0000.0790.0520.2670.1620.0560.1250.0350.2940.144
oh_OrderValueRefundedNaN0.9500.0210.094-0.111-0.182-0.182-0.1790.082-0.0360.6510.6620.032-0.3740.006-0.0980.6641.0000.0240.3130.0530.6840.2680.3410.069-0.1110.0740.2600.046-0.0030.2660.1760.2230.1760.2010.2560.0320.2780.0240.0610.0430.1330.1020.1420.0530.1000.2870.089
oh_PaymentMethod1.0001.0000.1370.6190.0620.0260.2770.0190.0000.1110.0000.0000.0000.2040.0000.0000.0000.0241.0000.6200.5180.0700.002-0.0040.619-0.0680.010-0.0200.016-0.017-0.0170.1680.0400.1220.000-0.0280.0160.0001.0000.000-0.0000.0000.0030.0280.5180.1830.0440.013
oh_ShippingCode1.0000.1370.1280.7450.1110.5370.6800.5330.4130.2980.4190.4190.0310.0710.0180.3230.4190.3130.6201.0000.7070.0140.008-0.0160.7450.0120.0320.005-0.069-0.0010.0130.2190.0570.0570.1010.005-0.0810.0490.6200.000-0.0000.4690.0450.0520.7070.2240.0010.040
oh_SourceCode1.0000.1300.1850.7460.0570.0640.0630.0630.1370.1910.1270.1260.0280.0630.0180.3240.1250.0530.5180.7071.0000.0620.0200.0780.7460.010-0.0820.019-0.011-0.0120.0100.3140.0070.0550.1050.019-0.0480.0260.5180.000-0.0030.0550.0280.0441.0000.2080.0240.037
oh_TaxValueNaNNaN0.0170.007-0.174-0.214-0.214-0.2110.111-0.0830.8950.9150.0110.0500.091-0.1060.9120.6840.0700.0140.0621.0000.3190.2910.084-0.1740.0890.3180.0250.0300.3260.1420.3570.0530.0250.3080.0110.0310.0210.0850.0230.0940.1470.0660.1270.1520.3740.130
ol_AmountPaidNaNNaN0.0240.000-0.129-0.099-0.099-0.0960.025-0.0200.2980.3040.0610.0080.010-0.0380.3050.2680.0020.0080.0200.3191.0001.0000.052-0.1290.1800.9870.0700.0370.9770.099-0.3550.3000.1320.9850.0610.1730.0520.0950.0990.7300.6550.4190.0650.1870.9600.543
ol_AmountRefundedNaN0.0260.026-0.038-0.028-0.046-0.046-0.0460.050-0.0120.2080.2220.1550.0190.041-0.0410.2220.341-0.004-0.0160.0780.2911.0001.0000.010-0.0280.0960.9990.1580.0110.9740.049-0.1370.0001.0000.9960.1551.0000.0260.1090.0800.3640.0691.0000.0910.3970.9900.000
ol_CampaignCode1.0000.0840.1291.0000.0550.2170.4470.2110.0180.0980.0890.0870.0000.0380.0140.0000.0880.0690.6190.7450.7460.0840.0520.0101.000-0.0380.003-0.0210.0140.005-0.0210.1930.0150.0350.113-0.0260.0200.0260.6190.0000.0070.0660.0160.0350.7460.2140.0130.019
ol_CustomerIDNaNNaN-0.058-0.0381.0000.0290.0290.0290.0380.014-0.160-0.1610.020-0.028-0.0590.002-0.160-0.111-0.0680.0120.010-0.174-0.129-0.028-0.0381.0000.010-0.1280.026-0.019-0.1230.0590.0000.0440.022-0.1250.0200.0110.0620.000-0.0050.6990.0950.0490.0570.056-0.1390.104
ol_DiscountValueNaNNaN-0.0410.0030.0100.0070.0070.0070.701-0.0590.0770.079-0.146-0.0180.023-0.0160.0790.0740.0100.032-0.0820.0890.1800.0960.0030.0101.0000.177-0.1450.0380.2370.000-0.0310.0500.0000.175-0.1460.0000.0000.1800.0040.0000.0980.0720.0780.0330.1790.092
ol_GrossValueNaNNaN0.026-0.021-0.128-0.096-0.096-0.0980.023-0.0130.3020.3070.0650.0070.049-0.0390.3060.260-0.0200.0050.0190.3180.9870.999-0.021-0.1280.1771.0000.0670.0360.9900.071-0.3610.3040.1320.9990.0650.1620.0630.0950.0460.7300.6600.4260.0710.1850.9670.549
ol_InvoiceNumberNaNNaN0.1520.0140.0260.0440.0440.044-0.2250.0440.0140.0170.9670.0150.019-0.1200.0160.0460.016-0.069-0.0110.0250.0700.1580.0140.026-0.1450.0671.000-0.0970.0530.140-0.0840.1800.4440.0690.9670.3530.0000.0000.0030.6750.0000.1700.1340.0190.0650.000
ol_ItemCostPriceNaNNaN-0.0460.005-0.019-0.019-0.019-0.0190.023-0.0100.0230.024-0.088-0.005-0.006-0.0070.024-0.003-0.017-0.001-0.0120.0300.0370.0110.005-0.0190.0380.036-0.0971.0000.0350.2530.1180.0000.0000.036-0.0880.0000.0260.000-0.0190.0000.0190.0140.0540.0000.0370.016
ol_ItemUnitPriceNaNNaN0.023-0.021-0.123-0.133-0.133-0.1350.068-0.0250.3090.3140.0510.0070.048-0.0380.3130.266-0.0170.0130.0100.3260.9770.974-0.021-0.1230.2370.9900.0530.0351.0000.071-0.3330.3040.1300.9890.0510.1610.0600.000-0.0110.7300.6500.4260.0700.1850.9560.551
ol_LastActionCode1.0000.6140.1870.1930.0590.1320.1450.1300.1180.5700.1460.1470.1540.3620.2270.0470.1440.1760.1680.2190.3140.1420.0990.0490.1930.0590.0000.0710.1400.2530.0711.0000.2390.8150.149-0.1120.3840.7980.1680.0000.1370.4240.3331.0000.3140.135-0.1040.314
ol_LineIDNaNNaN0.0080.0150.000-0.184-0.184-0.1820.0760.0120.4410.436-0.0940.0300.033-0.0520.4350.2230.0400.0570.0070.357-0.355-0.1370.0150.000-0.031-0.361-0.0840.118-0.3330.2391.0000.1460.040-0.360-0.0940.0620.0210.0680.0150.6020.2240.2050.0680.073-0.3570.262
ol_LineMajorStatus1.0000.5130.1530.0350.0440.0480.0400.0480.0360.2420.0430.0480.1320.6730.7940.0210.0470.1760.1220.0570.0550.0530.3000.0000.0350.0440.0500.3040.1800.0000.3040.8150.1461.0000.817-0.554-0.0110.8060.1220.016-0.0270.2300.4490.9690.0550.070-0.5400.426
ol_LineMinorStatus1.0000.5920.1470.1130.0220.0530.0530.0530.0000.3940.0420.0390.3150.6620.5760.0190.0380.2010.0000.1010.1050.0250.1321.0000.1130.0220.0000.1320.4440.0000.1300.1490.0400.8171.0000.2760.0450.9980.0000.0000.0070.6880.1801.0000.1050.1110.2720.500
ol_NetAmountNaNNaN0.028-0.026-0.125-0.095-0.095-0.0970.021-0.0070.3030.3060.0670.0060.048-0.0400.3050.256-0.0280.0050.0190.3080.9850.996-0.026-0.1250.1750.9990.0690.0360.989-0.112-0.360-0.5540.2761.0000.0670.1550.1060.0900.0490.6790.7510.4140.1200.1260.9570.530
ol_OrderIDNaNNaN0.1450.0200.0200.0370.0370.037-0.2280.037-0.003-0.0001.0000.0430.0560.061-0.0020.0320.016-0.081-0.0480.0110.0610.1550.0200.020-0.1460.0650.967-0.0880.0510.384-0.094-0.0110.0450.0671.0000.2580.0000.000-0.0280.3600.0000.0000.0280.0000.0640.000
ol_PaymentMajorStatus1.0000.5440.1110.0260.0110.0500.0500.0500.0060.6400.0500.0470.2580.5580.0360.0000.0470.2780.0000.0490.0260.0310.1731.0000.0260.0110.0000.1620.3530.0000.1610.7980.0620.8060.9980.1550.2581.0000.0000.0000.0460.2460.1640.9340.0260.1080.2690.000
ol_PaymentMethod1.0001.0000.1370.6190.0620.0260.2770.0190.0000.1110.0000.0000.0000.2040.0000.0000.0000.0241.0000.6200.5180.0210.0520.0260.6190.0620.0000.0630.0000.0260.0600.1680.0210.1220.0000.1060.0000.0001.0000.000-0.0000.0000.0030.0280.5180.1830.0440.013
ol_Quantity1.0001.0000.0000.0000.0000.0000.0000.0000.0480.1300.0770.0780.0000.0000.0000.0000.0790.0610.0000.0000.0000.0850.0950.1090.0000.0000.1800.0950.0000.0000.0000.0000.0680.0160.0000.0900.0000.0000.0001.0000.8301.0000.0350.0250.0000.0300.0580.033
ol_QuantityDespatchedNaNNaN0.0090.007-0.005-0.012-0.012-0.012-0.0070.0680.0390.037-0.028-0.094-0.171-0.0040.0520.043-0.000-0.000-0.0030.0230.0990.0800.007-0.0050.0040.0460.003-0.019-0.0110.1370.015-0.0270.0070.049-0.0280.046-0.0000.8301.0001.0000.0420.4010.0000.0310.0300.039
ol_ShippingCode1.0000.7760.6540.0660.6990.0000.0000.0000.1751.0000.1470.2670.3600.2780.0001.0000.2670.1330.0000.4690.0550.0940.7300.3640.0660.6990.0000.7300.6750.0000.7300.4240.6020.2300.6880.6790.3600.2460.0001.0001.0001.0000.6920.1670.0550.675-0.0330.000
ol_SkuStatus1.0001.0000.0000.0160.0950.1280.1290.1290.0180.0000.1660.1620.0000.0000.0170.0280.1620.1020.0030.0450.0280.1470.6550.0690.0160.0950.0980.6600.0000.0190.6500.3330.2240.4490.1800.7510.0000.1640.0030.0350.0420.6921.0000.4430.0280.179-0.7650.669
ol_SkuStockStatus1.0001.0000.0220.0350.0490.0520.0510.0520.0240.1740.0540.0570.0000.2790.3840.0000.0560.1420.0280.0520.0440.0660.4191.0000.0350.0490.0720.4260.1700.0140.4261.0000.2050.9691.0000.4140.0000.9340.0280.0250.4010.1670.4431.0000.0440.018-0.5770.421
ol_SourceCode1.0000.1300.1850.7460.0570.0640.0630.0630.1370.1910.1270.1260.0280.0630.0180.3240.1250.0530.5180.7071.0000.1270.0650.0910.7460.0570.0780.0710.1340.0540.0700.3140.0680.0550.1050.1200.0280.0260.5180.0000.0000.0550.0280.0441.0000.2080.0240.037
ol_TaxRate1.0001.0000.0490.2140.0560.0000.0720.0130.0250.2070.0560.0340.0000.0530.0120.0110.0350.1000.1830.2240.2080.1520.1870.3970.2140.0560.0330.1850.0190.0000.1850.1350.0730.0700.1110.1260.0000.1080.1830.0300.0310.6750.1790.0180.2081.0000.2730.165
ol_TaxValueNaNNaN0.0170.013-0.139-0.084-0.085-0.0830.034-0.0430.2810.2950.0640.0200.051-0.0340.2940.2870.0440.0010.0240.3740.9600.9900.013-0.1390.1790.9670.0650.0370.956-0.104-0.357-0.5400.2720.9570.0640.2690.0440.0580.030-0.033-0.765-0.5770.0240.2731.0000.518
ol_Weight1.0001.0000.0450.0190.1040.0260.0280.0270.0180.0000.1470.1450.0000.0000.0170.0340.1440.0890.0130.0400.0370.1300.5430.0000.0190.1040.0920.5490.0000.0160.5510.3140.2620.4260.5000.5300.0000.0000.0130.0330.0390.0000.6690.4210.0370.1650.5181.000

Missing values

2023-11-24T14:24:08.376915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T14:24:09.883681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-24T14:24:14.937744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RTYPESYS_CHANGE_VERSIONSYS_CHANGE_OPERATIONoh_ClientCodeoh_OrderIDoh_ExternalOrderIDoh_CustomerIDoh_CampaignCodeoh_SourceCodeoh_MediaIDoh_PaymentMethodoh_PaymentTypeoh_NetOrderValueoh_TaxValueoh_OrderGrossValueoh_OrderValuePaidoh_OrderValueRefundedoh_OrderVoucherValueoh_DeliveryTaxCodeoh_DeliveryNetValueoh_DeliveryTaxValueoh_DeliveryGrossValueoh_DeliveryReasonCodeoh_DiscountValueoh_DiscountReasonCodeoh_LoyaltyValueoh_OrderMethodoh_OrderTypeoh_OrderMajorStatusoh_OrderMinorStatusoh_CreatedDateoh_DespatchDateoh_CancelledDateoh_Priorityoh_UserIDoh_CurrencyCodeoh_CurrencyRateoh_LastActionCodeoh_DueDateoh_DeliverByDateoh_ShippingCodeoh_ShippingReasonCodeoh_PIIol_ClientCodeol_OrderIDol_ExternalOrderIDol_CustomerIDol_LineIDol_CreatedDateol_CampaignCodeol_SourceCodeol_MediaIDolProductIDol_Quantityol_QuantityDespatchedol_DespatchedDateol_LineMajorStatusol_LineMinorStatusol_SkuStatusol_SkuStockStatusol_PaymentMajorStatusol_PaymentMinorStatusol_PaymentMethodol_PaymentTypeol_CancelledDateol_LastActionCodeol_ParentItemNumberol_Priorityol_UserIDol_CurrencyCodeol_CurrencyRateol_ShippingCodeol_InvoiceNumberol_ItemCostPriceol_ItemUnitPriceol_NetAmountol_TaxValueol_GrossValueol_AmountPaidol_AmountRefundedol_PostageValueol_PostageTaxValueol_PostageReasonCodeol_DiscountValueol_DiscountReasonCodeol_TaxLocaleol_TaxRateol_Weightol_PII
0H10602.0UCRW5421459.01284148824432924.0WEBTWEBNaN2.0NaN25.005.0030.030.030.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2022-10-12 11:22:12.000NaNNaN0.01000.0GBP1.0NaN2022-10-12 11:22:12.0002022-10-12 11:22:12.000RMTNaN****CRW5421459.01284148824432924.01.02022-10-12 11:33:22.093WEBTWEBNaN12212901.01.02022-10-13 11:29:20.203CancelledRefund Confirmed10.00.0RefundedComplete2.0NaN2023-11-22 12:59:05.327RFI0.00.01000.0GBP1.0NaN6360410.00.030.025.005.0030.030.030.0NaNNaNNaN0.0NaNNaN20.00.00****
1H10602.0UCRW5421459.01284148824432924.0WEBTWEBNaN2.0NaN25.005.0030.030.030.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2022-10-12 11:22:12.000NaNNaN0.01000.0GBP1.0NaN2022-10-12 11:22:12.0002022-10-12 11:22:12.000RMTNaN****CRW5421459.01284148824432924.02.02022-10-12 11:33:22.157WEBTWEBNaNFRL-9991.01.02022-10-13 11:29:20.650Despatch ConfirmedNaN9999.010.0NaNNaN2.0NaNNaNNaN0.00.01000.0GBP1.0NaN6360410.00.00.00.000.000.00.0NaNNaNNaNNaN0.0NaNNaN20.00.00****
2H13287.0UCRW5864333.01315885323690258.0WEBTWEBNaN2.0NaN30.836.1737.037.037.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-03-09 23:27:42.000NaNNaN0.01000.0GBP1.0NaN2023-03-09 23:27:42.0002023-03-09 23:27:42.000RMTNaN****CRW5864333.01315885323690258.01.02023-03-09 23:42:11.780WEBTWEBNaN12508241.01.02023-03-10 18:41:18.597CancelledRefund Confirmed10.00.0RefundedComplete2.0NaN2023-11-22 16:17:11.283SAR0.00.01000.0GBP1.0NaN6888122.00.037.030.836.1737.037.037.0NaNNaNNaN0.0NaNNaN20.00.00****
3H13287.0UCRW5864333.01315885323690258.0WEBTWEBNaN2.0NaN30.836.1737.037.037.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-03-09 23:27:42.000NaNNaN0.01000.0GBP1.0NaN2023-03-09 23:27:42.0002023-03-09 23:27:42.000RMTNaN****CRW5864333.01315885323690258.02.02023-03-09 23:42:11.787WEBTWEBNaNFRL-9991.01.02023-03-10 18:41:19.083Despatch ConfirmedNaN9999.010.0NaNNaN2.0NaNNaNNaN0.00.01000.0GBP1.0NaN6888122.00.00.00.000.000.00.0NaNNaNNaNNaN0.0NaNNaN20.00.01****
4H13958.0UCRW5942595.01321719231653496.0WEBTWEBNaN2.0NaN22.504.5027.027.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-08 21:47:44.000NaNNaN0.01000.0GBP1.0NaN2023-04-08 21:47:44.0002023-04-08 21:47:44.000CCDPDNDNaN****CRW5942595.01321719231653496.01.02023-04-08 22:02:15.970WEBTWEBNaN12466961.01.02023-04-13 10:48:02.473CancelledFailed Payment Process10.00.0FailedRefundNaN2.0NaN2023-11-22 17:52:09.567RFI0.00.01000.0GBP1.0NaN6982897.00.08.06.671.338.08.0NaNNaNNaNNaN0.0NaNNaN20.00.00****
5H13958.0UCRW5942595.01321719231653496.0WEBTWEBNaN2.0NaN22.504.5027.027.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-08 21:47:44.000NaNNaN0.01000.0GBP1.0NaN2023-04-08 21:47:44.0002023-04-08 21:47:44.000CCDPDNDNaN****CRW5942595.01321719231653496.02.02023-04-08 22:02:15.983WEBTWEBNaN12553521.01.02023-04-11 08:02:25.197Despatch ConfirmedCharge Confirmed10.010.0ChargedComplete2.0NaNNaNNaN0.00.01000.0GBP1.0NaN6982897.00.019.015.833.1719.019.0NaNNaNNaNNaN0.0NaNNaN20.00.00****
6H13958.0UCRW5942595.01321719231653496.0WEBTWEBNaN2.0NaN22.504.5027.027.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-08 21:47:44.000NaNNaN0.01000.0GBP1.0NaN2023-04-08 21:47:44.0002023-04-08 21:47:44.000CCDPDNDNaN****CRW5942595.01321719231653496.03.02023-04-08 22:02:15.987WEBTWEBNaNINTRO1.01.02023-04-11 08:02:26.450Despatch ConfirmedNaN9999.010.0NaNNaN2.0NaNNaNNaN0.00.01000.0GBP1.0NaN6982897.00.00.00.000.000.00.0NaNNaNNaNNaN0.0NaNNaN20.00.01****
7H13958.0UCRW5942595.01321719231653496.0WEBTWEBNaN2.0NaN22.504.5027.027.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-08 21:47:44.000NaNNaN0.01000.0GBP1.0NaN2023-04-08 21:47:44.0002023-04-08 21:47:44.000CCDPDNDNaN****CRW5942595.01321719231653496.04.02023-04-08 22:02:15.990WEBTWEBNaNFRL-9991.01.02023-04-11 08:02:27.313Despatch ConfirmedNaN9999.010.0NaNNaN2.0NaNNaNNaN0.00.01000.0GBP1.0NaN6982897.00.00.00.000.000.00.0NaNNaNNaNNaN0.0NaNNaN20.00.01****
8H13566.0UCRW5983878.01324647419145903.0WEBTWEBNaN2.0NaN89.9918.01108.0108.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-30 13:29:48.000NaNNaN0.01000.0GBP1.0NaN2023-04-30 13:29:48.0002023-04-30 13:29:48.000CCHERMSTDNaN****CRW5983878.01324647419145903.01.02023-04-30 13:42:28.913WEBTWEBNaN11676121.01.02023-05-02 15:23:29.000SwappedNaN10.00.0ChargedComplete2.0NaN2023-05-11 11:42:07.973SI0.00.01000.0GBP1.0NaN7037174.00.040.033.336.6740.040.0NaNNaNNaNNaN0.0NaNNaN20.00.00****
9H13566.0UCRW5983878.01324647419145903.0WEBTWEBNaN2.0NaN89.9918.01108.0108.00.00.00.00.00.00.0NaN0.0NaN0.05.00.0Despatch ConfirmedNone2023-04-30 13:29:48.000NaNNaN0.01000.0GBP1.0NaN2023-04-30 13:29:48.0002023-04-30 13:29:48.000CCHERMSTDNaN****CRW5983878.01324647419145903.02.02023-04-30 13:42:28.923WEBTWEBNaN12604201.01.02023-05-02 15:23:30.000SwappedNaN10.00.0ChargedComplete2.0NaN2023-05-11 11:44:08.437SI0.00.01000.0GBP1.0NaN7037174.00.028.023.334.6728.028.0NaNNaNNaNNaN0.0NaNNaN20.00.00****
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82246HNaNICRWNaNNaNNaNWEBTWEBNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 17:14:23.000NaNNaNNaNNaNGBPNaNNaN2023-11-23 17:14:23.0002023-11-23 17:14:23.000RMTNaN****CRWNaNNaNNaNNaN2023-11-23 17:23:19.527WEBTWEBNaNFRL-999NaNNaNNaNReady For DespatchWaiting For Other TenderNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****
82247HNaNICRWNaNNaNNaNWEBTAWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 17:15:01.000NaNNaNNaNNaNGBPNaNNaN2023-11-23 17:15:01.0002023-11-23 17:15:01.000RMTNaN****CRWNaNNaNNaNNaN2023-11-23 17:23:24.630WEBTAWNaN1281204NaNNaNNaNReady For DespatchNaNNaNNaNAuthedCompleteNaNNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****
82248HNaNICRWNaNNaNNaNWEBTAWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 17:15:01.000NaNNaNNaNNaNGBPNaNNaN2023-11-23 17:15:01.0002023-11-23 17:15:01.000RMTNaN****CRWNaNNaNNaNNaN2023-11-23 17:23:24.637WEBTAWNaN1188024NaNNaNNaNReady For DespatchNaNNaNNaNAuthedCompleteNaNNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****
82249HNaNICRWNaNNaNNaNWEBTAWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 17:15:01.000NaNNaNNaNNaNGBPNaNNaN2023-11-23 17:15:01.0002023-11-23 17:15:01.000RMTNaN****CRWNaNNaNNaNNaN2023-11-23 17:23:24.643WEBTAWNaN1275375NaNNaNNaNReady For DespatchNaNNaNNaNAuthedCompleteNaNNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****
82250HNaNICRWNaNNaNNaNWEBTAWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 17:15:01.000NaNNaNNaNNaNGBPNaNNaN2023-11-23 17:15:01.0002023-11-23 17:15:01.000RMTNaN****CRWNaNNaNNaNNaN2023-11-23 17:23:24.650WEBTAWNaN1250439NaNNaNNaNReady For DespatchNaNNaNNaNAuthedCompleteNaNNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****
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Duplicate rows

Most frequently occurring

RTYPESYS_CHANGE_VERSIONSYS_CHANGE_OPERATIONoh_ClientCodeoh_OrderIDoh_ExternalOrderIDoh_CustomerIDoh_CampaignCodeoh_SourceCodeoh_MediaIDoh_PaymentMethodoh_NetOrderValueoh_TaxValueoh_OrderGrossValueoh_OrderValuePaidoh_OrderValueRefundedoh_OrderVoucherValueoh_DeliveryTaxCodeoh_DeliveryNetValueoh_DeliveryTaxValueoh_DeliveryGrossValueoh_DeliveryReasonCodeoh_DiscountValueoh_DiscountReasonCodeoh_LoyaltyValueoh_OrderMethodoh_OrderTypeoh_OrderMajorStatusoh_OrderMinorStatusoh_CreatedDateoh_CancelledDateoh_Priorityoh_UserIDoh_CurrencyCodeoh_CurrencyRateoh_LastActionCodeoh_DueDateoh_DeliverByDateoh_ShippingCodeoh_ShippingReasonCodeoh_PIIol_ClientCodeol_OrderIDol_ExternalOrderIDol_CustomerIDol_LineIDol_CreatedDateol_CampaignCodeol_SourceCodeol_MediaIDolProductIDol_Quantityol_QuantityDespatchedol_DespatchedDateol_LineMajorStatusol_LineMinorStatusol_SkuStatusol_SkuStockStatusol_PaymentMajorStatusol_PaymentMinorStatusol_PaymentMethodol_CancelledDateol_LastActionCodeol_ParentItemNumberol_Priorityol_UserIDol_CurrencyCodeol_CurrencyRateol_ShippingCodeol_InvoiceNumberol_ItemCostPriceol_ItemUnitPriceol_NetAmountol_TaxValueol_GrossValueol_AmountPaidol_AmountRefundedol_DiscountValueol_DiscountReasonCodeol_TaxLocaleol_TaxRateol_Weightol_PII# duplicates
0HNaNICRWNaN13717718NaNWEBTWEBNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNReady For DespatchNone2023-11-23 07:19:59.000NaNNaNNaNGBPNaNNaN2023-11-23 07:19:59.0002023-11-23 07:19:59.000RMTNaN****CRWNaN13717718NaNNaN2023-11-23 07:32:43.343WEBTWEBNaN1251434NaNNaNNaNTorque ProcessingAllocatedNaNNaNAuthedCompleteNaNNaNNaNNaNNaNNaNGBPNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN****2
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